Dissertations / Theses: 'Rotating Machines Diagnostic'

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SOAVE, Elia. "Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning." Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.

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In the last decades, the vibration analysis has been exploited for monitoring many mechanical systems for industrial applications. Although several works demonstrated how the vibration based diagnostics may reach satisfactory results, the nowadays industrial scenario is deeply changing, driven by the fundamental need of time and cost reduction. In this direction, the academic research has to focus on the improvement of the computational efficiency for the signal processing techniques applied in the mechanical diagnostics field. In the same way, the industrial word requires an increasing attention to the predictive maintenance for estimating the system failure avoiding unnecessary machine downtimes for maintenance operations. In this contest, in the recent years the research activity has been moved to the development of prognostic models for the prediction of the remaining useful life. However, it is important to keep in mind how the two fields are strictly connected, being the diagnostics the base on which build the effectiveness of each prognostic model. On these grounds, this thesis has been focused on these two different but linked areas for the detection and prediction of possible failures inside rotating machines in the industrial framework. The first part of the thesis focuses on the development of a blind deconvolution indicator based on the cyclostationary theory for the fault identification in rotating machines. The novel criterion aims to decrease the computational cost of the blind deconvolution through the exploitation of the Fourier-Bessel series expansion due to its modulated nature more comparable with the fault related vibration pattern. The proposed indicator is extensively compared to the other cyclostationary one based on the classic Fourier transform, taking into account both synthesized and real vibration signals. The comparison proves the improvement given by the proposed criterion in terms of number of operations required by the blind deconvolution algorithm as well as its diagnostic capability also for noisy measured signals. The originality of this part regards the combination of cyclostationarity and Fourier-Bessel transform that leads to the definition of a novel blind deconvolution criterion that keeps the diagnostic effectiveness of cyclostationarity reducing the computational cost in order to meet the industrial requirements. The second part regards the definition of a novel prognostic model from the family of the hidden Markov models constructed on a generalized Gaussian distribution. The target of the proposed method is a better fitting quality of the data distribution in the last damaging phase. In fact, the fault appearance and evolution reflects on a modification of the observation distribution within the states and consequently a generalized density function allows the changes on the distribution form through the values of some model parameters. The proposed method is compared in terms of fitting quality and state sequence prediction to the classic Gaussian based hidden Markov model through the analysis of several run to failure tests performed on rolling element bearings and more complex systems. The novelty of this part regards the definition of a new iterative algorithm for the estimation of the generalized Gaussian model parameters starting from the observations on the physical system for both monovariate and multivariate distributions. Furthermore, the strictly connection between diagnostics and prognostics is demonstrated through the analysis of a not monotonically increasing damaging process proving how the selection of a suitable indicator enables the correct health state estimation.
Negli ultimi decenni, l’analisi vibrazionale è stata sfruttata per il monitoraggio di molti sistemi meccanici per applicazioni industriali. Nonostante molte pubblicazioni abbiano dimostrato come la diagnostica vibrazionale possa raggiungere risultati soddisfacenti, lo scenario industriale odierno è in profondo cambiamento, guidato dalla necessità di ridurre tempi e costi produttivi. In questa direzione, la ricerca deve concentrarsi sul miglioramento dell’efficienza computazionale delle tecniche di analisi del segnale applicate a fini diagnostici. Allo stesso modo, il mondo industriale richiede una sempre maggior attenzione per la manutenzione predittiva, al fine di stimare l’effettivo danneggiamento del sistema evitando così inutili fermi macchina per operazioni manutentive. In tale ambito, negli ultimi anni l’attività di ricerca si sta spostando verso lo sviluppo di modelli prognostici finalizzati alla stima della vita utile residua dei componenti. Tuttavia, è importante ricordare come i due ambiti siano strettamente connessi, essendo la diagnostica la base su cui fondare l’efficacia di ciascun modello prognostico. Su questa base, questa tesi è stata incentrata su queste due diverse, ma tra loro connesse, aree al fine di identificare e predire possibile cause di cedimento su macchine rotanti per applicazioni industriali. La prima parte della tesi è concentrata sullo sviluppo di un nuovo indicatore di blind deconvolution per l’identificazione di difetti su organi rotanti sulla base della teoria ciclostazionaria. Il criterio presentato vuole andare a ridurre il costo computazionale richiesto dalla blind deconvolution tramite l’utilizzo della serie di Fourier-Bessel grazie alla sua natura modulata, maggiormente affine alla tipica firma vibratoria del difetto. L’indicatore proposto viene accuratamente confrontato con il suo analogo basato sulla classica serie di Fourier considerando sia segnali simulati che segnali di vibrazione reali. Il confronto vuole dimostrare il miglioramento fornito dal nuovo criterio in termini sia di minor numero di operazioni richieste dall’algoritmo che di efficacia diagnostica anche in condizioni di segnale molto rumoroso. Il contributo innovativo di questa parte riguarda la combinazione di ciclostazionarietà e serie di Furier-Bessel che porta alla definizione di un nuovo criterio di blind deconvolution in grado di mantenere l’efficacia diagnostica della ciclostazionarietà ma con un minor tempo computazionale per venire incontro alle richieste del mondo industriale. La second parte riguarda la definizione di un nuovo modello prognostico, appartenente alla famiglia degli hidden Markov models, costruito partendo da una distribuzione Gaussiana generalizzata. L’obbiettivo del metodo proposto è una miglior riproduzione della reale distribuzione dei dati, in particolar modo negli ultimi stadi del danneggiamento. Infatti, la comparsa e l’evoluzione del difetto comporta una modifica della distribuzione delle osservazioni fra i diversi stati. Di conseguenza, una densità di probabilità generalizzata permette la modificazione della forma della distribuzione tramite diversi valori dei parametri del modello. Il metodo proposto viene confrontato con il classico hidden Markov model di base Gaussiana in termini di qualità di riproduzione della distribuzione e predizione della sequenza di stati tramite l’analisi di alcuni test di rottura su cuscinetti volventi e sistemi complessi. L’innovatività di questa parte è data dalla definizione di un algoritmo iterativo per la stima dei parametri del modello nell’ipotesi di distribuzione Gaussiana generalizzata, sia nel caso monovariato che multivariato, partendo dalle osservazioni sul sistema fisico in esame.

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Neill, Gary David. "PC based diagnostic system for the condition monitoring of rotating machines." Thesis, Heriot-Watt University, 1998. http://hdl.handle.net/10399/1266.

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Hajar, Mayssaa. "Contribution of random sampling in the context of rotating machinery diagnostic." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES001/document.

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Récemment, le diagnostic des machines tournantes devient un des sujets de recherche les plus importants. Plusieurs axes sont développés dans ce domaine : traitement de signal, reconnaissance des formes et autres. En plus, les systèmes industriels peuvent être surveillés à distance en temps réel grâce à la disponibilité de l’internet. Cette surveillance se trouve exigeante au niveau de l’acquisition et le stockage des données. En 2004, le Compressive Sensing est introduit dans le but d’acquérir les données a une basse fréquence afin d’économiser l’énergie dans les réseaux de capteurs sans fils. Des résultats similaires peuvent être achevés par l’Echantillonnage Aléatoire qui procure une acquisition à basse fréquence grâce à sa propriété d’anti-repliement. Comme cette technique d’échantillonnage est jusqu’à l’instant de la rédaction de cette thèse n’est pas encore disponible au marché, le travail sur ce sujet se trouve promettant afin de présenter une implémentation pratique validée. D’où, la contribution de cette thèse est de présenter les différentes propriétés de l’échantillonnage aléatoire à travers une étude théorique détaillée dans le domaine temporel et fréquentiel suivie d’une simulation et d’une application pratique sur des signaux synthétisés simples puis sur des signaux de vibration extraits des principaux composants des machines : roulements et engrenages. Les résultats obtenus au niveau de la simulation et la pratique sont satisfaisants grâce à la diminution de la fréquence d’échantillonnage et la quantité de données à sauvegarder ce qui peut être considéré comme une résolution de la problématique de la surveillance à temps réel
Nowadays, machine monitoring and supervision became one of the most important domains of research. Many axes of exploration are involved in this domain: signal processing, machine learning and several others. Besides, industrial systems can now be remotely monitored because of the internet availability. In fact, as many other systems, machines can now be connected to any network by a specified address due to the Internet of Things (IOT) concept. However, this combination is challenging in data acquisition and storage. In 2004, the compressive sensing was introduced to provide data with low rate in order to save energy consumption within wireless sensor networks. This aspect can also be achieved using random sampling (RS). This approach is found to be advantageous in acquiring data randomly with low frequency (much lower than Nyquist rate) while guaranteeing an aliasing-free spectrum. However, this method of sampling is still not available by hardware means in markets. Thus, a comprehensive review on its concept, its impact on sampled signal and its implementation in hardware is conducted. In this thesis, a study of RS and its different modes is presented with their conditions and limitations in time domain. A detailed examination of the RS’s spectral analysis is then explained. From there, the RS features are concluded. Also, recommendations regarding the choice of the adequate mode with the convenient parameters are proposed. In addition, some spectral analysis techniques are proposed for RS signals in order to provide an enhanced spectral representation. In order to validate the properties of such sampling, simulations and practical studies are shown. The research is then concluded with an application on vibration signals acquired from bearing and gear. The obtained results are satisfying, which proves that RS is quite promising and can be taken as a solution for reducing sampling frequencies and decreasing the amount of stored data. As a conclusion, the RS is an advantageous sampling process due to its anti-aliasing property. Further studies can be done in the scope of reducing its added noise that was proven to be cyclostationary of order 1 or 2 according to the chosen parameters

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He, Jiamin. "Investigation on Diagnostic Methods of Rotating Machines and Influence Factors Based on Existing Testing Products." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277635.

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This thesis summarizes established methods for electrical diagnostics of the insulationof large rotating electrical machines, i.e. generators and large motorsworking above the low-voltage range. It then investigates the possibility ofusing some existing diagnostic instruments, not specically intended for machineinsulation, for performing standard tests on a certain type and range ofthe rotating machines. The summary of general diagnostic methods for rotatingmachinery includes the traditional methods and currently used methodsin industry. It considers what types of the defects can be detected, and theinuence of the applied voltage magnitudes and frequencies, etc. Then thereis a literature study of several recent or developing technologies such as on-linemonitoring and frequency response analysis, to investigate the possible futuredevelopment of the diagnostic methods that have practical applications duringmanufacturing and operation of rotating machinery for a more accurateand timely assessment. Possible modications to testing devices to suit themmore to machine insulation are investigated. A study of three market-existingdevices summarizes the machine diagnostic tests that they could be used for.Finally, an experimental study on a stator coil rated 7 kV is reported, and itsresults are used to analyze the inuence of the test factors such as frequencydependency, for future investigation.
Denna avhandling sammanfattar etablerade metoder för elektrisk diagnos-tik av isolering hos stora roterande elektriska maskiner, dvs generatorer och stora motorer med märkspänningar högre än lågspänning. Därefter undersöks möjligheten att använda vissa befintliga diagnostiska instrument, som inte är särskilt avsedda för maskinisolering, för att utföra standardtester på roterande maskinerna. I sammanfattningen av diagnostiska metoder för roterande maskiner ingår traditionella metoder och för närvarande använda metoder inom industrin. Den anser vilka typer av defekter kan upptäckas, och påverkan av de tillämpade spännings magnituder och frekvenser, etc. En litteraturstudieomfattar flera nya eller utvecklande teknologier såsom on-line övervakningoch frekvensresponsanalys, för att undersöka den möjliga framtida utvecklingen av de diagnostiska metoder som har praktiska tillämpningar under tillverkning och drift av roterande maskiner för en mer exakt och punktlig bedömning. Möjliga modifieringar av provningsanordningar som passar dem mer för maskin isolering undersöks. study av tre marknadsföra-existerande apparater sammanfattar de bearbeta med maskin diagnostiska testar att de kunde användas för. En experimentell studie på en 7 kV statorlindning rapporteras, och dess resultat används för att analysera påverkan av test faktorersåsom frekvensberoendet av resultaten, för framtida utredning.

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Kass, Souhayb. "Diagnostic vibratoire autonome des roulements." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI103.

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Le monde de l’industrie et des transports dispose de machines et d’installations de plus en plus performantes et complexes. Ils ne peuvent être exempts de perturbations et de défaillances, influant sur la qualité du produit, pouvant provoquer l’arrêt immédiat d’une machine et porter atteinte au bon fonctionnement d’un système de production entier. Le diagnostic de ces machines, s’appuie essentiellement sur la surveillance de symptômes liés à différentes conditions de dégradation. Ces symptômes peuvent être tirés de diverses sources d’information, parmi lesquelles l’analyse vibratoire et acoustique occupe une place prépondérante. Aujourd'hui, de nombreuses techniques efficaces sont bien établies, ancrées sur des outils puissants offerts notamment par la théorie des processus cyclostationnaires. La complexité de ces outils exige un expert pour les utiliser et les interpréter. La présence continue d’un expert est difficilement réalisable et couteuse. Des indicateurs d’état de machines tournantes existent dans la littérature mais ils sont conçus sous l'hypothèse de conditions de fonctionnement parfaites. Ces travaux restent limités, dispersés et généralement non soutenus par des cadres théoriques. Le principal objectif de cette thèse est de réduire le recours à l'intervention humaine en proposant des stratégies pour concevoir deux indicateurs optimaux qui résument l'information de diagnostic en une valeur scalaire. Ces stratégies sont élaborées en distinguant deux familles dans le diagnostic : le cas où les informations sur les défauts sont connues et celle où elles sont inconnues. Ces indicateurs sont destinés à être utilisés dans le cadre d'un processus de diagnostic autonome, sans nécessiter d’intervention humaine, à l’aide des tests d’hypothèses statistiques. La capacité de ces indicateurs est validée sur des données réelles et comparée avec d’autres indicateurs de la littérature en termes de performance de détection
The industrial and transportation sectors require more and more efficient and complex machines and installations increasing the risk of failure and disruption. This can lead to the immediate shutdown of a machine and disrupts the proper functioning of the entire production system. The diagnosis of industrial machines is essentially based on the monitoring of symptoms related to different degradation conditions. These symptoms can be derived from various sources of information, including vibration and acoustic signals. Nowadays, many effective techniques are well established, based on powerful tools offered by the theory of cyclostationary processes. The complexity of these tools requires an expert to use them and to interpret the results based on his/her experience. The continuous presence of the expert is expensive and difficult to achieve in practice. Condition indicators for rotating machines exist in the literature but they are conceived under the assumption of perfect operating conditions. They are limited, dispersed and generally not supported by theoretical frameworks. The main objective of this thesis is to reduce the use of human intervention by proposing strategies to design two optimal indicators that summarize diagnostic information into a scalar value. A distinction is made between two families in diagnosis: the case where prior information on the faults is known and the case where it is unknown. These indicators are designed to be used in an autonomous process without requiring human intervention, using statistical hypothesis tests. The capacity of these indicators is validated on real data and compared with other indicators from the literature in terms of detection performance

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Kohutek, Tomáš. "Diagnostika vibrací strojů při kusových zkouškách." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318531.

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This master thesis deals with the vibration diagnostics of machine vibrations during unit tests in industrial company Siemens Electric Machines s.r.o Drásov. In the master thesis is elaborated a design of the diagnostic system which contains selected methods of vibration diagnostics, methology, procedure of measurement and evaluation of measured values. The part of master thesis is also a practical example of measurement on which the mentioned system is tested.

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Karkafi, Fadi. "Nonstationary vibration diagnostics of rotating machinery : Application to aeronautic power transmission systems." Electronic Thesis or Diss., Lyon, INSA, 2024. http://www.theses.fr/2024ISAL0132.

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Le bon fonctionnement des machines tournantes repose sur la surveillance vibratoire de composants rotatifs fragiles tels que les engrenages et les roulements. Concernant plus particulièrement le cas des systèmes de transmission de puissance en aéronautique, la surveillance vibratoire présente des défis considérables qui sont abordés dans cette thèse : (i) les régimes de fonctionnement non stationnaires, qui nécessitent l'adoption d'approches synchrones, (ii) les interactions complexes entre différents sous-systèmes, susceptibles de masquer ou perturber les signaux de diagnostic et (iii) le bruit émis par diverses sources, tant environnementales qu’internes, rendant la détection des défauts plus difficile. Pour répondre à ces défis, les principes de diagnostic proposé dans cette thèse s'articulent autour de plusieurs objectifs : (1) une estimation fiable de la vitesse angulaire instantanée, permettant la synchronisation des signaux avec les variations du régime, (2) l'extraction des composantes vibratoires pertinentes pour isoler les composants mécaniques critiques et (3) l'application de diagnostics spécifiques à chaque composant, tenant compte des variations opérationnelles pour garantir robustesse et fiabilité. Les méthodologies développées sont validées par des données expérimentales, démontrant leur potentiel pour améliorer la fiabilité et la sécurité des systèmes de transmission en aéronautique
The proper functioning of rotating machines relies on vibration monitoring of fragile rotating components such as gears and bearings. Concerning more particularly the case of power transmission systems in aeronautics, vibration monitoring presents considerable challenges that are addressed in this thesis: (i) nonstationary operating regimes, which require the adoption of synchronous approaches, (ii) complex interactions between different subsystems, likely to mask or disturb diagnostic signals and (iii) noise emitted by various sources, both environmental and internal, making fault detection more difficult. To address these challenges, the diagnostic principles proposed in this thesis are structured around several objectives: (1) a reliable estimation of the instantaneous angular speed, allowing the synchronization of the signals with the variations of the regime, (2) the extraction of the relevant vibration components to isolate the critical mechanical components and (3) the application of specific diagnostics to each component, taking into account the operational variations to guarantee robustness and reliability. The developed methodologies are validated by experimental data, demonstrating their potential to improve the reliability and safety of transmission systems in aeronautics

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Menexiadis, Dimitri. "Conception d'un système expert d'aide au diagnostic pour les machines tournantes." Valenciennes, 1988. https://ged.uphf.fr/nuxeo/site/esupversions/04ba5d72-dc25-461d-8efd-ab79aeeefb8f.

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Fourati, Aroua. "Modélisation électro-magnéto-mécanique d'une machine asynchrone sous approche angulaire : Application au diagnostic des défauts de roulements en régime non stationnaire." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI078.

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Dans une machine à induction, le diagnostic de défauts par analyse du signal du courant électrique nécessite la connaissance du comportement dynamique de la machine. En plus des sources externes d'excitation, le comportement du moteur est gouverné par un ensemble de phénomènes périodiques liés sa géométrie angulairement périodique et couplés par leur caractère multiphysique. En présence d’un défaut de roulement, les grandeurs mesurables présenteront des composantes à sa fréquence caractéristique combinée aux fréquences caractéristiques du moteur. La compréhension des interactions, en particulier de modulation, passe par la mise en place de modèles numériques qui représentent les manifestations des phénomènes couplés. Ce travail de thèse propose donc un modèle électro-magnéto-mécanique d'une machine à induction à cage d'écureuil couplé à un modèle de palier à roulement à billes dans un cadre original d'écriture appelé "Approches Angulaires". En conservant dans la modélisation la relation "Angle-Temps" il est possible d'étendre aisèment la modélisation aux conditions de fonctionnement non-stationnaires et d'introduire un couplage fort entre les modèles mécanique et électromagnétique. Ainsi, on montre que la vitesse angulaire instantanée est la grandeur qui assure la transmission du défaut mécanique localisé aux grandeurs électriques. Le modèle proposé offre ainsi un décryptage des phénomènes de modulation présents sur la voie de transfert et décrits par les couplages de comportements dynamiques cycliques (réseau de perméances, chargement des éléments roulants,...) et/ou périodiques (résonances de structure, résonance électriques, ...). Ces travaux ouvrent la voie à une meilleure compréhension du comportement couplé multiphysique d'une machine électrique pour mieux spécifier les outils de surveillance à mettre en œuvre. Les futurs développements peuvent maintenant s'orienter ver une complexification des modèles ou l'exploitation de comportements dynamiques fins en régime non-stationnaire
In an induction machine, the diagnosis of defects by analysis of the electrical current signal requires knowledge of the dynamic behavior of the machine. In addition to external excitation sources, the behavior of the motor is governed by a set of periodic phenomena related to its angularly periodic geometry and coupled by their multiphysical character. In the presence of a bearing defect, measurable quantities will have components at its characteristic frequency combined with the characteristic frequencies of the engine. The understanding of interactions, in particular modulation, requires the implementation of numerical models that represent the manifestations of coupled phenomena. This thesis work proposes an electro-magneto-mechanical model of a squirrel-cage induction machine coupled to a rolling bearing model in an original writing frame called "Angular Approaches". By keeping the "Angle-Time" relation in modeling, it is possible to easily extend the modeling to non-stationary operating conditions and to introduce a strong coupling between the mechanical and electromagnetic models. Thus, it is shown that the instantaneous angular speed is the quantity which ensures the transmission of the localized mechanical defect to the electrical quantities. The proposed model thus offers a decryption of the modulation phenomena present on the transfer path and described by the couplings of cyclic dynamic behaviors (permeance network, loading of the rolling elements, etc.) and / or periodic (structural resonances, electrical resonance, etc.). This work opens the way for a better understanding of the multiphysical coupled behaviors of an electrical machine to better specify the monitoring tools to be used. Further developments can now be directed to a complexity of models or to the exploitation of fine dynamic behaviors in a non-steady operating conditions

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Bardou, Olivier. "Sur des methodes de surveillance et de diagnostic vibratoire de machines alternativesS." Grenoble INPG, 1994. http://www.theses.fr/1994INPG0015.

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Le but de ce travail est d'appliquer des methodes de traitement du signal et de reconnaissance des formes pour la surveillance des machines alternatives par les vibrations. Une synthese des methodes existantes pour la surveillance vibratoire de machines simples, comme les machines tournantes, est tout d'abord developpee. Cela nous permet de degager les limites des outils classiques d'analyse. La complexite des machines alternatives justifie alors l'utilisation d'outils de traitement du signal plus adaptes a l'extraction de parametres pertinents vis a vis du caractere non-stationnaire du signal. L'utilisation des methodes de reconnaissance des formes permet d'elaborer un outil de surveillance et de diagnostic automatique et incluant la possibilite de variations des conditions de fonctionnement. Ce dernier point represente un aspect important du diagnostic dans un contexte industriel. La methode decrite ici est relativement simple a mettre en uvre et peu couteuse d'un point de vue industriel. De plus, elle s'applique avec succes sur deux types de machines alternatives, un moteur diesel et un compresseur d'air

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Assoumane, Amadou. "Diagnostic des engrenages et des roulements par une analyse vibratoire en régime variable." Thesis, Orléans, 2018. http://www.theses.fr/2018ORLE2061.

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Depuis une dizaine d'années, plusieurs méthodes de traitement du signal vibratoire ont été développées pour le diagnostic des machines tournantes en régime stationnaire. Or, de plus en plus de machines sous surveillance fonctionnent en régime variable telles que les éoliennes, les concasseurs, etc... Les méthodes développées pour la surveillance et le diagnostic de ces machines en régime stationnaire ne sont plus adaptées en régime variable. Certains outils proposés pour le régime variable sont limités à des applications bien spécifiques ou offrent un cadre théorique qui limite leur utilisation dans les situations réelles. Le but de cette thèse est justement de pallier ces limitations, et ce, en proposant une nouvelle approche permettant d'analyser les signaux vibratoires acquis en régime variable. La stratégie mise en oeuvre dans cette thèse repose sur une modélisation du signal vibratoire dans l'espace d'état et une estimation H∞ des grandeurs caractéristiques de l’état de fonctionnement de la machine. Tout d’abord, nous avons décrit le signal vibratoire dans l'espace d'état grâce à une projection de l'enveloppe de chaque composante fréquentielle du signal sur la base canonique orthogonale. Ensuite, nous avons proposé une estimation de l’enveloppe. Cette approche d'estimation repose sur une optimisation minimax et consiste à minimiser le maximum de l'erreur d'estimation sans faire d'hypothèse sur la nature statistique des bruits du modèle d'état. Cette stratégie a conduit à ce que nous avons appelé dans cette thèse ‘estimateur BCOH∞’. La nouvelle approche proposée a été appliquée à des signaux synthétiques et expérimentaux pour le diagnostic de l’état des engrenages et des roulements en régime variable
Over the past ten years, several vibration signal-processing methods have been developed for the diagnosisof stationary rotating machines. However, more and more machines under surveillance operate in variable speed condition such as wind turbines, crushers, etc... The methods developed for the monitoring and diagnosis of these machines in steady state are no longer suitable for variable regimes. Some tools proposed for the variable regime case are limited to specific applications or offer a theoretical framework that limits their use in real situations. The purpose of this thesis is precisely to overcome these limitations, and this, by proposing a new approach to analyse the vibration signal acquired in variable regime. The strategy implemented in this thesis is based on a modelling of the vibration signal in the state space and an estimation H∞ of the characteristic quantities of the operating state of the machine. First, we described the vibration signal in the state space through a projection of the envelope of each frequency component of the signal on the orthogonal canonical basis. Then we proposed an estimate of the envelope. This estimation approach is based on a minimax optimization and consists of minimizing the maximum of the estimation error without making any assumptions about the statistical nature of the state model noise. This strategy ledto what we called in this thesis 'BCOH∞ estimator'. The proposed new approach has been applied to synthetic and experimental signals for the diagnosis of gears and bearings condition in variable speed

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Lizoul, Khalid. "Démodulation de signaux de vitesse instantanée pour le diagnostic et la surveillance des machines tournantes." Electronic Thesis or Diss., Lyon, 2021. http://www.theses.fr/2021LYSES023.

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Dans le cadre de la maintenance conditionnelle, l'analyse de la vitesse instantanée des machines tournantes a commencé à être considérée par la communauté scientifique et industrielle durant ces dernières années. Les résultats de travaux exploitants la vitesse instantanée pour des fins de diagnostic de défauts mécaniques ont un potentiel naissant de ce type d'analyse. Ainsi, les principales méthodes d'estimation de cette grandeur intrinsèque d'une machine tournante sont décortiquées. Une attention particulière est donnée aux méthodes exploitant le signal à partir d'un capteur angulaire échantillonné dans le temps. En premier lieu, Nous mettons en œuvre trois méthodes de démodulation issues de la littérature en les adaptant au au problème particulier de l'estimation de la vitesse instantanée. Nous analysons l'influence de deux paramètres essentielles : la résolution du capteur et le rapport signal/bruit (SNR) du signal capteur angulaire. Puis, nous nous intéressons à la caractérisation bruit spectral de la vitesse instantanée sous une perturbation additive aléatoire. Enfin, pour palier à la limite imposée par le système d'acquisition pour exploiter des résolutions élevées, nous proposons une méthode de démodulation haute fréquence capable d'acquérir le signal avec une basse fréquence d'échantillonnage
In the context of conditional maintenance, the analysis of the instantaneous speed of rotating machines has begun to be considered by the scientific and industrial community in the last years. The results of works exploiting the instantaneous speed for the purpose of diagnosis of mechanical defects have a potential emerging from this type of analysis. Thus, the main methods for estimating this intrinsic quantity of a rotating machine are dissected. Particular attention is given to methods exploiting the signal from an angular sensor sampled in time. First, we implement three demodulation methods from the literature by adapting them to the particular problem of instantaneous speed estimation. We analyze the influence of two essential parameters: the resolution of the sensor and the signal to noise ratio (SNR) of the angular sensor signal. Then, we focus on the characterization of the spectral noise of the instantaneous velocity under a random additive perturbation. Finally, to overcome the limitation imposed by the acquisition system to exploit high resolutions, we propose a high frequency demodulation method capable of acquiring the signal with a low sampling frequency

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Hedlund, Niklas. "Implementing VLF as diagnostic test for HV motors and generators : A comparative study of diagnostic tests performed at different frequencies." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393813.

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High voltage testing of the stator winding insulation is one of the most recognized methods used to determine the state of degradation in the insulation. HV tests performed at 0.1 Hz do have potential advantages compared to more traditional 50 Hz tests. This thesis therefore aims to perform and compare tan delta, capacitance and partial discharge measurements on stator windings when using a 0.1 Hz voltage source and a more traditional 50 Hz voltage supply. Several associated test parameters with considerable influence on the test results were varied during the tests. An associated data analysis followed that was focused on the differences and similarities of the analyzed parameters and the results due to the differences in frequency. The results show that there are substantial levels of noise present in the partial discharge measurements when utilizing the VLF voltage source. There are also more numerous partial discharges for VLF measurements than for regular power frequency measurements if the same amount of voltage cycles is considered. The generated patterns show similarities with those generated at 50 Hz, but a larger sample base is probably needed for more thorough conclusions. The tan delta/capacitance part of the test do indicate potential advantages compared to power frequency measurements regarding the sensitivity in the measurements.

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Hawwari, Yasmine. "Developement of some signal processing tools for vibro-acoustic based diagnosis of aeronautic machines." Electronic Thesis or Diss., Lyon, INSA, 2022. https://theses.insa-lyon.fr/publication/2022ISAL0131/these.pdf.

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Le prétraitement des signaux de vibration dans des conditions difficiles comme celles de l'aéronautique semble être compliqué. Les conditions de fonctionnement sont nonstationnaires et le moteur présente au moins deux familles harmoniques non-linéaires liées à l'arbre à basse et haute pression. De plus, les contraintes de conception imposent un nombre réduit d'accéléromètres (généralement deux) qui est insuffisant pour détecter tous les phénomènes liés à l'arbre. Les signaux acoustiques ne sont pas soumis à cette dernière contrainte. Cependant, ils sont très bruyants par rapport aux signaux de vibration et peuvent ne pas détecter les problèmes de faible énergie. De plus, ils dépendent fortement de la position du microphone et de sa directivité. Ainsi, l'objectif de la thèse est de proposer/essayer des méthodes robustes pour principalement (i) l'interférence entre différents phénomènes linéaires et non linéaires, (ii) les conditions de fonctionnement non stationnaires et (iii) les phénomènes de bruit à large bande (lorsqu'ils ne sont pas d'intérêt). Ces difficultés scientifiques sont considérées à travers (1) une détection aveugle des pics spectraux, (2) l'estimation de la vitesse instantanée et (3) l'estimation de la composante déterministe/tonale
Pre-processing vibration signals in harsh conditions such as the aeronautic conditions seems a complicated task. The operating conditions are nonstationary and the motor exhibits at least two harmonic non-linear families related to low and high pressure shaft. Furthermore, the design constraints impose a reduced number of accelerometers (generally two) which is unfortunately insufficient to detect all the shaft related phenomena. The acoustic signals are not subjected to the latter constraint. However, they are very noisy in comparison to vibration signals and may not detect low energy problems and very low frequency phenomena. Besides, the obtained signals depend strongly on the microphone position and its directivity in addition to the problem of clipping with medium to high acoustic pressure values. Thus, the PhD objective is to propose methods robust to mainly (i) the interference between different linear and non-linearly related phenomena, (ii) the nonstationary operating conditions and (iii) the broadband noise phenomena. These scientific difficulties are considered through (1) a blind detection of spectral peaks, (2) the estimation of the instantaneous speed and (3) the estimation of the deterministic/tonal component

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Gubran, Ahmed. "Vibration diagnosis of blades of rotating machines." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/vibration-diagnosis-of-blades-of-rotating-machines(40f1d466-b393-42f6-a65a-e16801f06920).html.

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Rotating blades are considered to be the one of the most common cause of failures in rotating machinery. Blade failure modes normally occur as a result of cracks due to unexpected operating conditions, which are normally caused by accidents of foreign objects damage, high cycle fatigue, blade rubbing, blade root looseness, and degradation from erosion and corrosion. Thus, detection of blade faults has an important role in reducing blade related failures and allowing repairs to be scheduled for the machinery. This in turn will lead to reduction in maintenance costs and thus raise productivity and safety aspects of operation. To maintain vital components of rotating machines, such as blades, shafts, bearings and gear boxes, at optimal levels, detection of failures in such components is important, because this will prevent any serious damage that could affect performance. This research study involves laboratory tests on a small rig with a bladed disc rotor that applied vibration measurements and analysis for blade fault detection. Three measurements: shaft torsional vibration, on-bearing vibration (OBV) and on-casing vibration (OCV), are used. A small test rig of a single stage bladed disc holding 8-blades was designed and manufactured, to carry out this research study to assess the usefulness and capability of each vibration technique in detection of incipient defects within machine blades. A series of tests was conducted on a test rig for three different cases of blade health conditions: (a) healthy blade(s) with mistuned effects, (b) blade root looseness and (c) cracks in a blade on two different blade sizes (long and short blades) in order to discover changes in blades' dynamic behaviour during the machine running-up operation. The data were collected using the three measurements during machine run-up and then recorded. The measured vibration data were analysed by computing the blades' resonance at different engine orders (EOs) related to the blade(s) resonance frequencies and their higher harmonics, to understand the blade(s) dynamics behaviour for the cases of healthy and faulty blade(s). Data have been further processed using a polar plot presentation method which provides clear results that can be used for monitoring blade integrity. To validate the obtained experimental results, a simplified mathematical model was also developed. Finally, a comparative study between three methods was undertaken to understand the relative advantages and limitations in the blade heath monitoring.

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Edwards, S. "Fault diagnosis of rotating machinery." Thesis, Swansea University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636771.

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In this thesis, topics of importance to the fault diagnosis of rotating machinery in the power generation industry have been addressed, including a review of the relevant literature and an overview of the associated rotordynamics modelling and analysis techniques. For faults involving rotor-stator interaction it has been shown that the inclusion of torsion in mathematical models used for rotor-stator contract analyses can have a significant influence on the dynamic behaviour of the system. A 3 degrees-of-freedom model based on the Jeffcott rotor was developed and, for physically realistic systems, it was shown that very different results were obtained when including torsion, compared to when torsion was neglected, as has generally been the case in the past. An identification method for estimating both the excitation and flexible support parameters of a rotor-bearings-foundations system has been presented. Excitation due to both mass unbalance and a bent rotor were included in the analysis, which has been verified both in simulation and experimentally. The method has great practical potential, since it allows balancing to be performed using data obtained from just a single run-up or run-down, which has obvious benefits for field balancing. Using this single-shot balancing technique in experiment, vibration levels were successfully reduced by as much as 92% of their original levels. A bent rotor has been accurately identified in both simulation and experiment. It was also shown that including bend identification in those cases where only unbalance forcing was present in no way detracted from the accuracy of the estimated unbalance or foundation parameters. The identification of the flexible foundation parameters was generally successful, with measured and estimated parameters matching very closely in most cases. The identification method was tested for a wide range of conditions and proved suitably robust to changes in the system configuration, noisy data and modelling error.

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Cédric, Peeters. "Advanced signal processing for the identification and diagnosis of the condition of rotating machinery." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI107.

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Cette thèse porte sur des méthodes innovantes de contrôle de l'état de santé des machines tournantes par l’analyse des signaux vibratoires. En effet, la surveillance de l’état de santé des machines contribue à des améliorations substantielles des points de vue économique et de sureté. Afin d’y aboutir, l’une des manières les plus populaires est de recueillir les vibrations de la machine. La plupart de ces vibrations sont directement liées au comportement périodique des sous-systèmes de la machine tels que les arbres de rotation, engrenages, champs électriques rotationnels, etc. Cette connaissance peut être exploitée afin de concevoir une méthodologie adaptée à chaque type de défaut. Cette thèse s’intéresse aux étapes de la mise en œuvre de cette méthodologie. En règle générale, la première condition préalable à l’analyse avancée de l’information récoltée est la disponibilité de la vitesse instantanée de rotation. Cette vitesse doit être connue car la plupart des techniques du traitement du signal sont adaptées aux conditions de fonctionnement stationnaires. Ainsi, la connaissance de la vitesse permettra de compenser les fluctuations de vitesse, par exemple par le ré-échantillonnage angulaire du signal de vibration. Malgré l’existence d’outils de mesure permettant l’estimation de la vitesse tels que les codeurs et les tachymètres, cette thèse étudie le potentiel d’estimer la vitesse instantanée de rotation à partir des signaux vibratoires. Après l'estimation de la vitesse et le ré-échantillonnage angulaire, une étape suivante courante consiste à séparer le signal en composantes déterministes et stochastiques. Dans ce sens, l’efficacité et l’applicabilité de la procédure d'édition du cepstre sont analysées. Ensuite, différentes méthodes de filtrage sont appliquées au signal résiduel afin d’améliorer le rapport signal sur bruit. Pour cette fin, les méthodes existantes utilisant des critères conventionnels sont étudiées en parallèles avec une nouvelle méthodologie aveugle de filtrage. La dernière étape du processus de traitement consiste à diagnostiquer le défaut potentiel. Ainsi, des indicateurs statistiques sont calculés sur le signal obtenu après traitement et suivis dans le temps pour vérifier leurs variations. Dans de nombreux cas, la signature du défaut présente un comportement cyclostationaire. Par conséquent, cette thèse examine également différentes techniques d'analyse de la cyclostationarité. Enfin, les performances des différentes méthodes de traitement sont validées sur deux ensembles de données expérimentales de vibrations issues de boîtes de vitesses d’éoliennes
This Ph.D. dissertation targets innovative methods for vibration-based condition monitoring of rotating machinery. Substantial benefits can be achieved from an economical and a safety point of view using condition monitoring. One of the most popular methods to gather information about the state of machine parts is through the analysis of machine vibrations. Most of these vibrations are directly linked to periodical behavior of subsystems within the machine like e.g. rotating shafts, gears, rotating electrical fields, etc. This knowledge can be exploited to enable faultdependent processing schemes. This dissertation investigates how to implement and utilize these processing schemes and details the steps in such a procedure. Typically, the first prerequisite for advanced analysis is the availability of the instantaneous rotation speed. This speed needs to be known since most frequency-based analysis techniques assume stationary behavior. Knowledge of the speed thus allows for compensating speed fluctuations, for example through angular resampling of the vibration signal. While there are hardware-based solutions for speed estimation using angle encoders or tachometers, this thesis investigates the potential in vibration signals for speed estimation. After speed estimation and angular resampling, a common next step is to separate the signal into deterministic and stochastic components. The cepstrum editing procedure is examined for its efficacy and applicability. Afterwards, different filtering methods are inspected as to improve the signal-to-noise ratio of the signal content of interest. Existing methods using conventional criteria are investigated together with a novel blind filtering methodology. The final step in the multi-step processing scheme is to search for the potential fault. Statistical indicators can be calculated on the processed time domain signal and tracked over time to check for increases. In many cases, the fault signature exhibits cyclostationary behavior. Therefore this dissertation also examines different cyclostationary analysis techniques. Lastly, the performance of the different processing methods is validated on two experimental vibration data sets of wind turbine gearboxes

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Zhu, Hui. "Partial discharge propagation, measurement, and calibration in high power rotating machines." Thesis, Glasgow Caledonian University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261609.

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Wang, Xian Bo. "A novel fault detection and diagnosis framework for rotating machinery using advanced signal processing techniques and ensemble extreme learning machines." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3951596.

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Xin, Ge. "Sparse representations in vibration-based rolling element bearing diagnostics." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI051/document.

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Bien que le diagnostic des roulements par analyse vibratoire soit un domaine très développé, la recherche sur les représentations parcimonieuses des signaux de vibration est encore nouvelle et difficile pour le diagnostic des machines tournantes. Dans cette thèse, de méthodes nouvelles ont été développées, au moyen de différents modèles stochastiques, associées à des algorithmes efficaces afin de servir l’industrie dans le diagnostic des roulements. Tout d’abord, les modèles parcimonieux présentés dans la littérature sont revus. Les principales publications concernant le diagnostic des machines tournantes ont également été considérées. Enfin, en discutant des avantages et des inconvénients des représentations parcimonieuses, une interprétation des structures creuses d’un point de vue Bayésien est proposée, ce qui donne lieu à deux nouveaux modèles de diagnostic des machines tournantes. Dans un second temps, un nouveau modèle stochastique est proposé : il introduit une variable cachée relative à l’apparition d’impacts et estime le contenu spectral des transitoires correspondants ainsi que le spectre du bruit de fond. Cela donne lieu à un algorithme de détection automatique - sans besoin de pré-filtrage manuel - à partir duquel les fréquences de défaut peuvent être révélées. Le même algorithme permet également de filtrer le signal de défaut de manière très efficace par rapport à d’autres approches basées sur l’hypothèse stationnaire. La performance de l’algorithme est étudiée sur des signaux synthétiques. L’efficacité et la robustesse de la méthode sont également vérifiées sur les signaux de vibration mesurés sur un banc d’essai (engrenages et paliers). Les résultats sont meilleurs ou au moins équivalents à ceux de l’analyse d’enveloppes classique et du kurtogramme rapide. Dans un troisième temps, un nouveau schéma pour l’extraction de signaux cyclostationnaires (CS) est proposé. En considérant la variance périodique en tant que variable cachée, un filtre temporel est conçu de manière à obtenir la reconstruction intégrale des signaux CS caractérisés par une fréquence cyclique préétablie, qui peut être connue à priori ou estimée à partir de la corrélation spectrale. Un intérêt particulier de la méthode est sa robustesse lorsqu’elle est appliquée sur des données expérimentales ainsi qu’une capacité d’extraction supérieure par rapport au filtre de Wiener conventionnel. Finalement, ces exemples expérimentaux témoignent de l’utilisation polyvalente de la méthode à des fins de diagnostic de signaux composés. Pour finir, une analyse comparée utilisant le calcul rapide de la corrélation spectrale est réalisée sur une base de données publiquement disponible et largement utilisée. C’est un point crucial qui fixe un défis non-trivial à résoudre
Although vibration-based rolling element bearing diagnostics is a very well-developed field, the research on sparse representations of vibration signals is yet new and challenging for machine diagnosis. In this thesis, several novel methods have been developed, by means of different stochastic models, associated with their effective algorithms so as to serve the industry in rolling element bearing diagnostics. First, the sparsity-based model (sparse code, in natural image processing) is investigated based on the current literature. The historical background of sparse representations has been inquired in the field of natural scenes. Along three aspects, its mathematical model with corresponding algorithms has been categorized and presented as a fundamental premise; the main publications are therefore surveyed in the literature on machinery fault diagnosis; finally, an interpretation of sparse structure in the Bayesian viewpoint is proposed which then gives rise to two novel models for machinery fault diagnosis. Second, a new stochastic model is introduced to address this issue: it introduces a hidden variable to indicate the occurrence of the impacts and estimates the spectral content of the corresponding transients together with the spectrum of background noise. This gives rise to an automatic detection algorithm – with no need of manual prefiltering as is the case with the envelope spectrum – from which fault frequencies can be revealed. The same algorithm also makes possible to filter out the fault signal in a very efficient way as compared to other approaches based on the stationary assumption. The performance is investigated on synthetic signals with a high noise-to-signal ratio and also in the case of a mixture of two independent transients. The effectiveness and robustness of the method are also verified on vibration signals measured on a test-bench (gears and bearings). Results are found superior or at least equivalent to those of conventional envelope analysis and fast kurtogram. Third, a novel scheme for extracting cyclostationary (CS) signals is proposed. By regularizing the periodic variance as hidden variables, a time-varying filter is designed so as to achieve the full-band reconstruction of CS signals characterized by some pre-set characteristic frequency. Of particular interest is the robustness on experimental data sets and superior extraction capability over the conventional Wiener filter. It not only deals with the bearing fault at an incipient stage, but it even works for the installation problem and the case of two sources, i.e. bearing and gear faults together. Eventually, these experimental examples evidence its versatile usage on diagnostic analysis of compound signals. Fourth, a benchmark analysis by using the fast computation of the spectral correlation is provided. One crucial point is to move forward the benchmark study of the CWRU data set by uncovering its own unique characteristics

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Kar, Rahul. "Diagnostics of subsynchronous vibrations in rotating machinery - methodologies to identify potential instability." Thesis, Texas A&M University, 2005. http://hdl.handle.net/1969.1/2596.

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Rotordynamic instability can be disastrous for the operation of high speed turbomachinery in the industry. Most ??instabilities?? are due to de-stabilizing cross coupled forces from variable fluid dynamic pressure around a rotor component, acting in the direction of the forward whirl and causing subsynchronous orbiting of the rotor. However, all subsynchronous whirling is not unstable and methods to diagnose the potentially unstable kind are critical to the health of the rotor-bearing system. The objective of this thesis is to explore means of diagnosing whether subsynchronous vibrations are benign or have the potential to become unstable. Several methods will be detailed to draw lines of demarcation between the two. Considerable focus of the research has been on subharmonic vibrations induced from non-linear bearing stiffness and the study of vibration signals typical to such cases. An analytical model of a short-rigid rotor with stiffness non-linearity is used for numerical simulations and the results are verified with actual experiments. Orbits filtered at the subsynchronous frequency are shown as a diagnostic tool to indicate benign vibrations as well as ??frequency tracking?? and agreement of the frequency with known eigenvalues. Several test rigs are utilized to practically demonstrate the above conclusions. A remarkable finding has been the possibility of diagnosing instability using the synchronous phase angle. The synchronous phase angle ?? is the angle by which the unbalance vector leads the vibration vector. Experiments have proved that ?? changes appreciably when there is a de-stabilizing cross coupled force acting on the rotor as compared to when there is none. A special technique to calculate the change in ?? with cross-coupling is outlined along with empirical results to exemplify the case. Subsequently, a correlation between the synchronous phase angle and the phase angle measured with most industrial balancing instruments is derived so that the actual measurement of the true phase angle is not a necessity for diagnosis. Requirements of advanced signal analysis techniques have led to the development of an extremely powerful rotordynamic measurement teststand ?? ??LVTRC??. The software was developed in tandem with this thesis project. It is a stand-alone application that can be used for field measurements and analysis by turbomachinery companies.

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Lowes, Suzanne. "A comparative study between condition monitoring techniques for rotating machinery." Thesis, University of Birmingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325615.

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Elnady, Maged Elsaid. "On-shaft vibration measurement using a MEMS accelerometer for faults diagnosis in rotating machines." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/onshaft-vibration-measurement-using-a-mems-accelerometer-for-faults-diagnosis-in-rotating-machines(cf9b9848-972d-49ff-a6b0-97bef1ad0e93).html.

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The healthy condition of a rotating machine leads to safe and cheap operation of almost all industrial facilities and mechanical systems. To achieve such a goal, vibration-based condition monitoring has proved to be a well-accepted technique that detects incipient fault symptoms. The conventional way of On-Bearing Vibration Measurement (OBVM) captures symptoms of different faults, however, it requires a relatively expensive setup, an additional space for the auxiliary devices and cabling in addition to an experienced analyst. On-Shaft Vibration Measurement (OSVM) is an emerging method proposed to offer more reliable Faults Diagnosis (FD) tools with less number of sensors, minimal processing time and lower system and maintenance costs. The advancement in sensor and wireless communications technologies enables attaching a MEMS accelerometer with a miniaturised wireless data acquisition unit directly to the rotor without altering the machine dynamics. In this study, OSVM is analysed during constant speed and run-up operations of a test rig. The observations showed response modulation, hence, a Finite Element (FE) analysis has been carried out to help interpret the experimental observations. The FE analysis confirmed that the modulation is due to the rotary motion of the on-shaft sensor. A demodulation method has been developed to solve this problem. The FD capability of OSVM has been compared to that of OBVM using conventional analysis where the former provided more efficient diagnosis with less number of sensors. To incorporate more features, a method has been developed to diagnose faults based on Principal Component Analysis and Nearest Neighbour classifier. Furthermore, the method is enhanced using Linear Discriminant Analysis to do the diagnosis without the need for a classifier. Another faults diagnosis method has been developed that ensures the generalisation of extracted faults features from OSVM data of a specific machine to similar machines mounted on different foundations.

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Paya, Basir Abdul. "Vibration condition monitoring and fault diagnostics of rotating machinery using artificial neural networks." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390220.

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Emmanouilidis, Christos. "Evolutionary multi-objective feature selection and its application to industrial machinery fault diagnosis." Thesis, University of Sunderland, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391024.

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Ainapure, Abhijeet Narhar. "Application and Performance Enhancement of Intelligent Cross-Domain Fault Diagnosis in Rotating Machinery." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623164772153736.

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Nembhard, Adrian. "On-bearing vibration response integration for condition monitoring of rotating machinery." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/onbearing-vibration-response-integration-for-condition-monitoring-of-rotating-machinery(f713f156-11f3-4e10-846e-0b9b709f0ff9).html.

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Vibration-based fault diagnosis (FD) with a simple spectrum can be complex, especially when considering FD of rotating machinery with multiple bearings like a multi-stage turbine. Various studies have sought to better interpret fault spectra, but the process remains equivocal. Consequently, it has been accepted that the simple spectra requires support from additional techniques, such as orbit analysis. But even orbit analysis can be inconclusive. Though promising, attempts at developing viable methods that rival the failure coverage of spectrum analysis without gaining computational complexity remain protracted. Interestingly, few researchers have developed FD methods for transient machine operation, however, these have proven to be involved. Current practices limit vibration data to a single machine, which usually requires a large unique data history. However, if sharing of data between similar machines with different foundations was possible, the need for unique histories would be mitigated. From readily available works, this has not been encountered. Therefore, a simple but robust vibration-based approach is warranted. In light of this, a novel on-bearing vibration response integration approach for condition monitoring of shaft-related faults irrespective of speed and foundation type is proposed in the present study. Vibration data are acquired at different speeds for: a baseline, unbalance, bow, crack, looseness, misalignment, and rub conditions on three laboratory rigs with dynamically different foundations, namely: rigid, flexible support 1 (FS1) and flexible support 2 (FS2). Testing is done on the rigid rig set up first, then FS1, and afterwards FS2. Common vibration features are computed from the measured data to be input to the proposed approach for further processing. First, the proposed approach is developed through its application to a machine at a steady speed in a novel Single-speed FD technique which exploits a single vibration sensor per bearing and fusion of features from different bearings for FD. Initially, vibration features are supplemented with bearing temperature readings with improved classification compared to vibration features alone. However, it is observed that temperature readings are insensitive to faults on the FS1 and FS2 rigs, when compared to vibration features, which are standardised for consistent classification on the different rigs tested. Thus, temperature is not included as a final feature. The observed fault classifications on the different rigs at different speeds with the standardised vibration features are encouraging. Thereafter, a novel Unified Multi-speed FD technique that is based on the initial proposed approach and which works by fusion of vibration features from different bearings at different speeds in a single analysis step for FD is proposed. Experiments on the different rigs repeatedly show the novel Multi-speed technique to be suitable for transient machine operation. Then, a novel generic Multi-foundation Technique (also based on the proposed approach) that allows sharing of vibration data of a wide range of fault conditions between two similarly configured machines with similar speed operation but different foundations is implemented to further mitigate data requirements in the FD process. Observations made with the rigs during steady and transient speed tests show this technique is applicable in situations where data history is available on one machine but lacking on the other. Comparison of experimental results with results obtained from theoretical simulations indicates the approach is consistent. Thus, the proposed approach has the potential for practical considerations.

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Carlson, David K. "Artificicial [i.e. Artificial] neural networks and their applications in diagnostics of incipient faults in rotating machinery." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28000.

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Pavlík, Josef. "Vybrané problémy s diagnostiky izolačních systémů točivých elektrických strojů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-233987.

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This dissertation thesis deals with the measurement of insulation resistance for rotating electrical machines and polarization indices calculated from them. The first part contains a short theoretical introduction, methods of measurement and basic formulas for calculations. The second part discusses the results obtained in laboratory measurements in both the model and the real coil of high voltage machine. There are also elucidated some of the principles and causes of some phenomena with which the measurement of insulation resistance is encountered. The third part deals with the results of measurements on real machines. There are mainly discussed the dependences of insulation resistance and polarization index on the influences that occur in measurements such as temperature measured insulation, moisture in the insulation, but also the influence of measuring instruments on the measured values. It is also expressed how much these factors affect the measurement results. In addition, this part deals with some other influences that have a negative affect on the measurement of insulation resistance. There is processed a new methodology for measuring insulation resistence in the fourth part of this thesis. The need to develop a metodology of measuring is based on the needs of engineering practice, where is considerable inconsistency of measurement in the present time. Measurement, and often performed on the same machine, are not nowadays often comparable, because measurements are not met even the basic rules resulting from the findings of research and development, which were discovered in the last few decades. Measurement of insulation resistance in our nowdays methodology stagnated on the level of the seventies of 20th century. For this reason, we have developed a new methodology of measurement that takes into account all significant influences affecting the measurement. The purpose of the methodology is to ensure full repeatability and comparability of measurements not only on the same machine but on machines of the same type, in optimal cases, the machines of different types. There are the chapters "The objectives of the work" and "Conclusion" the part of the work. A very important chapter is "The contribution of thesis", which summarizes the original results of this work and results, the use of which is expected in engineering practice.

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BUZZONI, Marco. "Development and validation of Blind Deconvolution and Empirical Mode Decomposition techniques for impulsive fault diagnosis in rotating machines." Doctoral thesis, Università degli studi di Ferrara, 2018. http://hdl.handle.net/11392/2478776.

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La diagnosi di difetti in macchine rotanti basata sull’analisi vibrazionale ha raggiunto una soddisfacente fase di maturità, essendo disponibili numerose metodologie consolidate per la rilevazione e l’identificazione di difetti. Tuttavia, diverse problematiche restano ancora aperte; questa tesi ne prende in considerazione due. Da un lato, la ciclostazionarietà non è stata ancora utilizzata esplicitamente per progettare criteri di deconvoluzione cieca per la diagnosi di macchine rotanti, sebbene l'importanza di applicare la ciclostazionarietà per scopi diagnostici sia stata ampiamente riconosciuta. Dall’altro, la localizzazione di un difetto che si verifica in una ruota dentata installata in un albero intermedio di un riduttore a più stadi, particolarmente complessa per la sovrapposizione di più sorgenti di vibrazione, non è stata ancora oggetto di studi. In questo contesto, basandosi sulla teoria dei processi ciclostazionari, la tesi affronta questi due aspetti, differenti ma correlati e complementari, relativi all'identificazione di difetti localizzati in ingranaggi e cuscinetti volventi. La prima parte della tesi propone un metodo di deconvoluzione cieca, basato sul quoziente di Rayleigh generalizzato, risolto mediante un algoritmo iterativo di decomposizione agli autovalori. Questo approccio è caratterizzato dalla presenza di una matrice di pesatura che guida il processo di deconvoluzione, grazie alla quale il metodo può essere facilmente adattato a criteri arbitrari. Un nuovo criterio basato sulla massimizzazione della ciclostazionarietà del secondo ordine viene proposto e confrontato con altri metodi di deconvoluzione cieca esistenti in letteratura. Il confronto, effettuato su segnali simulati e segnali sperimentali, ha dimostrato che l’algoritmo è efficace nella stima delle eccitazioni ciclostazionarie a partire da risposte vibratorie sia a regimi stazionari sia a regimi non stazionari. Questo metodo è validato attraverso due diversi casi sperimentali relativi ad un rotismo ordinario a due stadi e ad un cuscinetto volvente. L'originalità di questa parte riguarda l'introduzione di un nuovo algoritmo di deconvoluzione cieca basato su di un criterio ciclostazionario che consente l'estrazione di sorgenti ciclsotazionarie aventi una determinata frequenza ciclica. Sulla base di questo metodo, sono proposti inoltre due procedure originali per la diagnosi di cuscinetti e ingranaggi. In particolare, queste procedure si basano sul criterio ciclostazionario massimizzato mediante il metodo di deconvoluzione cieca che consente la diagnosi del difetto in termini di tipologia e di severità. La seconda parte riguarda lo sviluppo e la validazione di un metodo per l'identificazione di difetti localizzati presenti in una ruota dentata calettata su un albero intermedio di un rotismo ordinario multi-stadio. In questo contesto, si propone una metodologia che combina la Empirical Mode Decomposition e la media sincrona per separare il segnale ciclostazionario del primo ordine relativo alle ruote dentate sincrone, montate sul medesimo albero, in un insieme di segnali rappresentativi relativi alle singole ruote dentate. I modi oscillatori fisicamente significativi sono selezionati attraverso un criterio basato sui coefficienti di correlazione di Pearson. Il rilevamento dei guasti viene eseguito successivamente mediante indicatori di condizione dedicati. In aggiunta agli indicatori di condizione standard, sono proposti due nuovi indicatori di condizione sensibili alle variazioni di energia del segnale sul passo della ruota, che si sono dimostrati particolarmente efficaci per il rilevamento dei difetti localizzati. L’efficacia della metodologia proposta è esaurientemente discussa mediante l’applicazione a segnali simulati e da due set di dati sperimentali. In tutti i casi esaminati, i risultati mostrano la capacità di identificare con successo la ruota difettosa nei casi di più ruote calettate sullo stesso albero.
Vibration analysis provides a useful aid for monitoring many mechanical systems and industrial processes. In recent years, the vibration-based diagnosis of machines and mechanical systems has reached a satisfactory stage of maturity. Several established signal processing methodologies are now available for detecting and identifying localized faults, especially for gears and bearings. However, several questions are still open. Among them, this thesis addresses two correlated issues. On the one hand, cyclostationarity has not been explicitly used to design blind deconvolution criteria for machine diagnosis before now, although the importance to take advantage of cyclostationarity for diagnostics purpose has been widely recognized. Concurrently, the localization of a gear fault occurring in a gear located in an intermediate shaft of a multi-stage gearbox can be particularly complex due to the superposition of vibration signatures of different synchronous wheels. Nevertheless, this issue has not been investigated yet. On these grounds, this thesis has been focused on these two different but complementary facets about impulsive fault identification in rotating machines both rooted in the cyclostationary framework. The first part of the thesis focuses on a blind deconvolution method based on the generalized Rayleigh quotient and solved by means of an iterative eigenvalue decomposition algorithm. This approach is characterized by the presence of a weighting matrix that drives the deconvolution process, whereby it can be easily adapted to arbitrary criteria. A novel criterion based on the maximization of the cyclostationarity of the signal is proposed and compared with the other blind deconvolution methods existing in the literature. The proposed algorithm is extensively compared taking into account cyclostationary synthetic signals and real ones, demonstrating superior capability to recover cyclostationary sources both in stationary regimes and non-stationary regimes. This method is successfully validated for diagnostic purposes through two different experimental cases consisting of a gear tooth spall and an outer race bearing fault. The originality of this part mainly regards the introduction of a novel blind deconvolution algorithm based on a cyclostationary criterion that allows for the extraction of cyclostationary sources having a given cyclic frequency. Two original and consistent diagnostic protocols for bearing and gear diagnosis are proposed as well. In particular, these diagnostic procedures take advantage of the maximized cyclostationary criterion computed by way of the proposed blind deconvolution method allowing the diagnosis in terms of fault type and severity. The second part addresses a method for the identification of gear tooth faults occurring in a wheel located in the intermediate shaft of multi-stage gearboxes. In this context, this part introduces a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the first-order cyclostationary signal of the synchronous gears mounted on the same shaft into a set of representing signals of the single gears. The physical meaningful modes are selected by means of a criterion based on Pearson’s correlation coefficients and the fault detection is performed by dedicated condition indicators. The proposed methodology is exhaustively discussed and supported by simulated examples as well as two experimental datasets. This original strategy successfully identifies the faulty gear in both the experimental tests and therefore can be considered reliable for the identification of a faulty gear when the fault occurs in a shaft with multiple gears. Furthermore, two novel condition indicators sensitive to signal energy variations on the circular pitch are proposed and proved to be effective for the local gear fault detection.

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Moustafa, Wael. "Élaboration d'un système de suivi de l'endommagement de composants mécaniques fonctionnant en régime variable et à très basse vitesse : Application au diagnostic sur roulements." Thesis, Reims, 2016. http://www.theses.fr/2016REIMS030.

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Dans la majorité des secteurs industriels, les coûts de maintenance des systèmes de production représentent une partie non négligeable de l’ensemble de ces coûts. Pour améliorer les performances et la fiabilité des systèmes, différentes stratégies de maintenance sont utilisées. L’objectif est d’être capable de détecter toutes défaillances potentielles susceptibles d’affecter la production. L’analyse vibratoire est aujourd’hui un des outils les plus performants en termes de détection et de suivi de ces défaillances. Cependant, l’application de cette technique reste difficile dans le cas de machines fonctionnant avec des vitesses de rotation faibles et variables. Dans la présente thèse, nous proposons une technique alternative à l’analyse vibratoire pour surveiller l’état de fonctionnement de ces machines. Nous nous intéresserons plus particulièrement au suivi des roulements. Cette technique alternative se base sur l’analyse des variations de la vitesse angulaire instantanée qui sont générées par un défaut de roulement. Cette technique sera testée sur un banc d’essai et comparée avec d’autres techniques telles que l’analyse des signaux vibratoires et ultrasonores pour différentes conditions de chargement et pour différents types de défauts de roulement. Cette étude montre une grande efficacité de cette méthode dans le cas de très basses vitesses de rotation, constantes ou variables. L’ensemble de cette méthodologie a ensuite été implémentée sur un four de diffusion d’une unité de production sucrière. Les mesures réalisées montrent que cette technique est une technique prometteuse pour le suivi de systèmes fonctionnant avec des vitesses de rotation faibles et variables
In the majority of industrial sectors, production systems’ maintenance costs represent an important part of the whole process’s cost. In order to improve systems’ reliability and performance, different maintenance strategies are used. The goal is to be able to detect all potential malfunction that may affect the production. Nowadays, vibration analysis is one of the best tools in term of detection and defect monitoring. However, the application of these vibration based techniques remain difficult in the case of machines functioning at low speeds. In this thesis, we propose an alternative technique of vibration analysis for the surveillance of these machines that operate on very low speeds and variable speeds. We are specifically interested in bearings’ monitoring. This alternative technique is based on instantaneous angular speed’s variations analysis that are induced by a bearing defect. This technique is tested on a test bench and compared with other techniques such as vibration and ultrasound analysis for different loading conditions and for different bearing fault types. This study shows a high efficiency of this method in the case of very low speeds, constant and variable. This methodology has then been implemented on sugar diffusion oven in a sugar production factory. The measurements shows that this technique is a promising technique for machines’ surveillance in very low and variable speeds working conditions

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Boonyaprapasorn, Arsit. "FAULT DETECTION AND DIAGNOSIS PROCESS FOR CRACKED ROTOR VIBRATION SYSTEMS USING MODEL-BASED APPROACH." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1238469531.

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M, Vulović Stevan. "Integrisani model održavanja zasnovan na uspostavljanju zakonitosti promene mehaniĉkih vibracija i njegov uticaj na prognostiku stanja rotacionih mašina." Phd thesis, Univerzitet u Novom Sadu, Tehnički fakultet Mihajlo Pupin u Zrenjaninu, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=106743&source=NDLTD&language=en.

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Osnovni cilj ove disertacije je da se razvije Integrisani model odrţavanja zasnovan na vibracijama sloţenih rotacionih tehniĉkih sistema, odnosno da se uspostavi sprovoĊenje dijagnostiĉkih provera stanja sklopova rotacionih mašina (kontrola vibracija). Zatim da se definišu optimalne periodiĉnosti vibracija kao i identifikacija ocena i rangiranja rizika sa stanovišta prekida rada mašina. Na taj naĉin potvrdiće se glavna hipoteza koja glasi: „Razvijanjem integrisanog modela odrţavanja zasnovanog na uspostavljanju zakonitosti promene mehaniĉkih vibracija moći će da se preventivno predvide pojave neispravnosti i prognozira stanje rotacionih mašina.
The basic goal of this dissertation is the development of an Integrated Maintenance Model based on vibrations of complex rotational technical systems, in other words, the establishment of implementation of diagnostic checks of rotating machinery compositions condition (control of vibrations). Afterwards, the definition of optimal periodicity of vibrations, as well as identification of estimations and ranking of risks from the stand point of disruption of work of operational processes. This is the way to confirm the main hypothesis which reads: “Development of an Integrated Maintenance Model based on the establishment of legality of change of mechanical vibrations will enable preventive predicting of malfunction occurrence, as well as prognosis of rotating machinery health.

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Martínek, Marek. "Tvorba SW pro generování signálu simulující závady rotačních systémů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442837.

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This diploma thesis deals with the design and creation of an algorithm for generating simulated signal data from a vibration diagnostics device. The first part is focused on theoretical acquaintance with vibration diagnostics and characteristics of individual defects of rotary machines. The next part deals with the possibilities of mathematical and kinematic simulations using a computer software. The main part of this work is dedicated to design and creation of software for generating simulated signal data. In the last part, the principle of simulation of specific defects of rotary machines is clearly demonstrated.

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Yang, Ching Su, and 楊肅慶. "Fault Diagnosis of Vibrational Signature For Rotating Machinery." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/26920383185174982346.

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碩士
中原大學
機械工程學系
87
Rotating machines should be kept in good condition, during operation in high speed. When elements of a machine fails, vibrations and damages are resulted. Hence, the distinguishing of abnormal signature are need before the failure happens. In this study, the misalignment, single tooth defects, and defect in outer race of bearing are considered by using methods of time waveform, the averaging of time series, spectrum analysis and modal analysis the differences between the normal signal and the faulty one to establish the characteristics of faulty signal, The inference mechanism is used to establish expert system for diagnosis.

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Tsai, Jen-Chen, and 蔡鎮丞. "Fault Diagnosis System Development for Rotating Electrical Machinery." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/00907609306541719105.

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碩士
正修科技大學
電機工程研究所
99
The thesis proposes a artificail neural network(ANN) for rotor machinery fault diagosis system. The fault detection is important for the the electrical rotor machinery. The breakdown of the rotor electrical machinery, especially the important machines, will bring about interruption of production and reduce profits. If there is a predictive fault diagnosis system, the situation of the outage can be avoided. However, the components of the rotor machinery are more complex and more sophisticated recently. These causes resulting in the machine vibration problems become more various and complicated. The mechanical vibration signal is a major parameter for the predictive maintenance system. The subject draws much attention in the predictive detection research. However, the electrical signal is also an important response when the electrical rotor machinery is breakdown. The thesis propose a excellent fault detection method, which is not only vibration signal considered but also electrical signal. The ANN is used to forecast for the fault diagnosis system in the study. The operation situation of machine can be detected by the parameters of operational pressure, temperature, and vibration. The thesis, also proposes the input current of machinery for more precisely diagnosing the equipment operation situation. These parameters are used to set the training patterns for the ANN to develop the fault diagnosis systems. Keywords: rotor electrical machinery, fault diagnosis, artificial neural network

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Lu, Wei-Chueng, and 盧威全. "The development of PC-based rotating machinery monitoring and diagnostic techniques." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/49516927047779157449.

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Gwu, Jhih-Wuei, and 辜志偉. "Study of Rotating Machine Fault Diagnosis System Using Neural Network." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/04811851187522357960.

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碩士
國立臺灣海洋大學
機械與輪機工程學系
92
Machine fault diagnosis system was to be respected for industry as a result of producing automatically with less and less operators. It is not easy to design fault diagnosis system for a complex and non-linear system with traditional mathematical module analysis. Neural network has the learning ability from training and tolerance for a slight change, and therefore it is more suitable for the complex and non-linear system especially. In this thesis, the network parameters trained from Levenberg-Marquardt method are to be used to classify the condition of rotating machine by back propagation algorithm. Simulates bi-directional acoustic emission like human hearing and employs multi-microphones system to acquire vibration sound signal brought on rotating machine. The spectrum analysis technique can extract signal features from frequency domain and these features were to be taken as input of neural network that can diagnose fault accurately. The diagnosis system in this study can detect motor condition on-line whether in normal state or not by using graphical user interface. It can diagnose not only single motor condition on-line, but also extend to several motors conditions at the same time.

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Shi, His-Tzu, and 許希孜. "Fault Diagnosis of Rotating Machine Based on Distributed Neural Network." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/y3k985.

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碩士
國立臺北科技大學
自動化科技研究所
95
This thesis develops a fault diagnosis system of rotating machine based on distributed neural network algorithm. The experimental rotor system, produced by Bently Nevada Corporation, is used to stimulate the specified rotating machine faults such as the mass unbalance, shaft bow, misalignment, oil whirl and oil whip etc. The generated vibration signals are feeding into the well learned fault diagnosis system to identify the corresponding faults. The fault diagnosis system includes two stages: 1) pre-processing of vibration signals, 2) diagnosis of faults. In first stage, the fixed sample-rate order tracking technology is firstly applied to trace the rotor speed, accurately catch and display the frequency of the orders of rotor speed. Then, the principal axis of full spectrum with plane information of two sensors is used to emphasize the vibration phenomenon than traditional half spectrum. Finally, the original signal compensation is applied to eliminate the initial malfunction, so the real signal due to these specified malfunctions can be extracted. In second stage, the datum after pre-processing are feeding into the well learned distributed neural network diagnosis system which can identify the type of malfunction and recognize the grade of multiple malfunctions. Finally, a single and double malfunctions generated by the rotor system are used to verify the performance of this fault diagnosis system. The experimental results reveal that this diagnosis system is suitable to identify the rotating machine faults.

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Huang, Chin-Wei, and 黃晉緯. "Application of Adaptive Algorithm in Rotating Machinery Fault Diagnosis." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/12364907085113479843.

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碩士
大葉大學
機械工程研究所碩士班
92
In this study, an application of adaptive order tracking fault diagnosis technique based on Recursive Least-Square algorithm, Kalman algorithm and variable step-size affine projection algorithm (VSS APA) is presented. Order tracking fault diagnosis technique is one of the important tools for fault diagnosis of rotating machinery. Conventional methods of order tracking are primarily based on Fourier analysis with reference to shaft speed. In this study, a high-resolution order tracking method with RLS algorithm or recursive Kalman algorithm or VSS APA is used to diagnose the fault in a gear set. The RLS algorithm, recursive Kalman algorithm and VSS APA can overcome the problems encountered in conventional methods. The problem is treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with high resolution after experimental implementation. Experiments are also carried out to evaluate the proposed system in gear set defect diagnosis. The experimental results indicated that the proposed algorithms are effective in gear set fault diagnosis.

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Carlson, Jon. "Model based predictive monitoring and diagnosis for rotating machinery." 1991. http://catalog.hathitrust.org/api/volumes/oclc/23964492.html.

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Thesis (M.S.)--University of Wisconsin--Madison, 1991.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 187-192).

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Chen, Jiun-Yo, and 陳俊佑. "Fault Diagnosis Using Electrical Signal for Rotating Electrical Machinery." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/mbntv4.

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碩士
正修科技大學
電機工程研究所
100
The thesis proposes artificial neural network (ANN) to a fault diagnosis system for rotor electrical machinery. The operation of machinery will generate mechanical vibration. However, it may be a fault signal when the vibration become abnormal enlarged. The research use rotor perturbation system of rotor machinery to simulate the fault signal and use triaxial accelerometer to measure the signal. The frequency domain signal can be obtained by Fourie transformation. After sorting the eigenvalue of frequency spectrum, the signal can be used as training data for ANN. The current harmonic signal and mechanical vibration signal are used in the thesis to ANN for fault diagnose. The trained ANN is used for diagnosis fault type of rotor machinery. There are 11 fault signal simulated in the thesis by the rotor simulation system for the fault forecasting system. The results show that vibration and current harmonic signal both have excellent diagnosis outcome.

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Srinivasan, K. S. "Fault diagnosis in rotating machines using vibration monitoring and artificial neural networks." Thesis, 2002. http://localhost:8080/iit/handle/2074/5115.

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Srinivasan, K. S. "Fault diagnosis in rotating machines using vibration monitoring and artificial neural networks." Thesis, 2002. http://localhost:8080/xmlui/handle/12345678/4506.

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Huang, Jiamin, and 黃嘉閔. "Diagnosis of rotating machinery using an intelligent order tracking system." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/94719382954462733015.

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碩士
國立交通大學
機械工程系
91
The aim of this research is to develop an on-line monitoring and diagnostic system for rotating machinery. Conventional methods of order tracking are primarily based on Fourier analysis with reference to shaft speed. Resampling process is generally required in the fast Fourier transform (FFT)-based methods to compromise between time and frequency resolution for various shaft speeds. Conventional methods suffer from a number of shortcomings. In particular, smearing problem arises when closely spaced orders or crossing orders are present. Conventional methods also are ineffective for the applications involving multiple independent shaft speeds. In this proposal, we use adaptive order tracking techniques based on Recursive Least-Squares (RLS) filtering and Kalman filtering to overcome the problems encountered in conventional methods. The architecture of the system mainly comprises a signal processing module and a state inference module. In the signal processing module, we use RLS or Kalman filter method to track the order features of vibration signal. In state inference module, the fuzzy expert system is applied. Through these two modules, the on-line monitoring system is approached.

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Lai, You-Hung, and 賴宥宏. "Rotating Machinery Fault Diagnosis System Using sGA-based Neural Networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/r8tu85.

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碩士
國立臺北科技大學
自動化科技研究所
97
This thesis proposes a robust fault diagnosis system of rotating machine adapting machine learning technology. The kernel of this diagnosis system includes a structure genetic algorithm neural network (sGANN). First, the frequency characteristics from differential fault signals are obtained by order tracking and full spectrum. The characteristic are used to feed into the sGANN corresponding to specified faults to emphasize the phenomenon of each fault. Especially, the structure genetic algorithm is applied to get the optimal parameters of the above sGANNs. In the final step of proposed diagnosis system, the evaluated indexes from sGANN are synthesized by a reasoning engine to identify the faults in the rotor system. In the experiment, six common malfunctions of rotor system, unbalance, misalignment, bow, rub, whirl and whip, are generated from a rotor kit to verify the performance of this diagnosis system. The advantage of this diagnosis system is that the optimal sGANN parameter can be automatically obtained, the local optimal can be reduced and the diagnosis accuracy can be improved.

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Vinson, Robert G. "Rotating machine diagnosis using smart feature selection under non-stationary operating conditions." Diss., 2015. http://hdl.handle.net/2263/43764.

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This dissertation investigates the effectiveness of a two stage fault identification methodology for rotating machines operating under non-stationary conditions with the use of a single vibration transducer. The proposed methodology transforms the machine vibration signal into a discrepancy signal by means of smart feature selection and statistical models. The discrepancy signal indicates the angular position and relative magnitude of irregular signal patterns which are assumed to be indicative of gear faults. The discrepancy signal is also independent of healthy vibration components, such as the meshing frequency, and effects of fluctuating operating conditions. The use of the discrepancy signal significantly reduces the complexity of fault detection and diagnosis. The first stage of the methodology involves extracting smart instantaneous operating condition specific features, while the second stage requires extracting smart instantaneous fault sensitive features. The instantaneous operating condition features are extracted from the coefficients of the low frequency region of the STFT of the vibration signal, since they are sensitive to operating condition changes and robust to the presence of faults. Then the sequence of operating conditions are classified using a hidden Markov model (HMM). The instantaneous fault features are then extracted from the coefficients in the wavelet packet transform (WPT) around the natural frequencies of the gearbox. These features are the converse to the operating condition features,since they are sensitive to the presence of faults and robust to the fluctuating operating conditions. The instantaneous fault features are sent to a set of Gaussian mixture models (GMMs), one GMM for each identified operating condition which enables the instantaneous fault features to be evaluated with respect to their operating condition. The GMMs generate a discrepancy signal, in the angular domain, from which gear faults may be detected and diagnosed by means of simple analysis techniques. The proposed methodology is validated using experimental data from an accelerated life test of a gearbox operated under fluctuating load and speed conditions.
Dissertation (MEng)--University of Pretoria, 2015.
Mechanical and Aeronautical Engineering
Unrestricted

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Chen, Chin-Hao, and 陳志豪. "Rotating Machinery Diagnosis Using Wavelet Packets-Fractal Technology and Neural Networks." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/64459842461732104547.

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博士
國立臺灣海洋大學
系統工程暨造船學系
95
The faults feature extraction is one of the most important research topics in the field of mechanical fault diagnosis. It is a issue which hinders the mechanical fault diagnosis technique from further improvement. Therefore, in this study, the problem of faults feature extraction and diagnosis was addressed using the signal processing technology. The methods combine signal processing tecdhnique with neural network to present a new fault diagnosis procedure for rotating. In the thesis divides seven chapter, at first, it gives a general view of fault diagnosis for rotating mechanical including developments and present situations. Power cepstrum, bispectrum and wavelet transform methods were discussed, and overview to the fractal dimension and neural network were also included. These methods are then used to perform faults diagnoses of rotational machinery systems. Form the results, it is shown that a combination of the these signal analysis tools give a more reliable condition monitoring method for rotary machinery. When faults occur they usually produce nonstationary vibration signals, by using wavelet packets transform on these signals, the fractal dimension of each frequency bands is extracted and the box dimension is used to depict the failure characteristics of vibration signals. Then the failure modes can be classified by radial basis function neural network. Experiments were conducted and the results shown that the proposed method can detect and recognize different kinds of faults in rotating machinery. Therefore, it is concluded that the wavelet packets-fractal technology combined with neural network method can provide an effective way to diagnosis faults in mechanical systems.

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Wang, KeSheng. "Approaches to the improvement of order tracking techniques for vibration based diagnostics in rotating machines." Thesis, 2011. http://hdl.handle.net/2263/28747.

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Conventional rotating machine vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the rotating machine. However, due to variations of the rotational speed, the measured signal may be non-stationary and difficult to interpret. For this reason, the order tracking technique is introduced. One of main advantages of order tracking over traditional vibration monitoring lies in its ability to clearly identify non-stationary vibration data and to a large extent exclude the influences of varying rotational speed. In recent years, different order tracking techniques have been developed. Each of these has their own pros and cons in analyzing rotating machinery vibration signals. In this research, three existing order tracking techniques are extensively investigated and combined to further explore their abilities in the context of condition monitoring. Firstly, computed order tracking is examined. This allows non-stationary effects due to the variation of rotational speed to be largely excluded. However, this technique was developed to deal with the entire raw signal and therefore looses the ability to focus on each individual order of interest. Secondly, Vold-Kalman filter order tracking is considered. It is widely reported that this technique overcomes many of the limitations of other order tracking methods and extracts order signals into the time domain. However because of the adaptive nature of the Vold-Kalman filter, the non-stationary effects due to the rotational speed will remain in the extracted order waveform, which is not ideal for conventional signal processing methods such as Fourier analysis. Yet, the strict mathematical filter (the Vold-Kalman filter is based upon two rigorous mathematical equations, namely the data equation and the structural equation, to realize the filter) gives this technique an excellent ability to focus on the orders of interest. Thirdly, the empirical mode decomposition method is studied. In the literature, this technique is claimed to be an effective diagnostic tool for various kinds of applications including diagnosis of rotating machinery faults. Its unique empirical way of extracting non-stationary and non-linear signals allows it to capture machine fault information which is intractable by other order tracking methods. But since there is no precise mathematical definition for an intrinsic mode function in empirical mode decomposition and – as far as could be ascertained – no published assessment of the relationship between an order and an intrinsic mode function, this technique has not been properly considered by analysts in terms of order tracking. As a result, its abilities have not really been explored in the context of order related vibrations in rotating machinery. In this research, the relationship between an order and an intrinsic mode function is discussed and it is treated as a special kind of order tracking method. In stead of focusing individually on each order tracking technique, the current work synthesizes different order tracking techniques. Through combination, exchange and reconciliation of ideas between these order tracking techniques, three improved order tracking techniques are developed for the purpose of enhancing order tracking analysis in condition monitoring. The techniques are Vold-Kalman filter and computed order tracking (VKC-OT), intrinsic mode function and Vold-Kalman filter order tracking (IVK-OT) and intrinsic cycle re-sampling (ICR). Indeed, these improved approaches contribute to current order tracking practice, by providing new order tracking methods with new capabilities for condition monitoring of systems which are intractable by traditional order tracking methods, or which enhances results obtained by these traditional methods. The work commences with a discussion of the inter-relationship between the order tracking methods which are considered in the thesis, and exposition of the scope of the work and an explanation of the way these independent order tracking techniques are integrated in the thesis. To demonstrate the abilities of the improved order tracking techniques, two simulation models are established. One is a simple single-degree-of-freedom (SDOF) rotor model with which VKC-OT and IVK-OT techniques are demonstrated. The other is a simplified gear mesh model through which the effectiveness of the ICR technique is proved. Finally two experimental set-ups in the Sasol Laboratory for Structural Mechanics at the University of Pretoria are used for demonstrating the improved approaches for real rotating machine signals. One test rig was established to monitor an automotive alternator driven by a variable speed motor. A stator winding inter-turn short was artificially introduced. Advantages of the VKC-OT technique are presented and features clear and clean order components under non-stationary conditions. The diagnostic ability of the IVK-OT technique of further decomposing an intrinsic mode function is also demonstrated via signals from this test rig, so that order signals and vibrations that modulate orders in IMFs can be separated and used for condition monitoring purposes. The second experimental test rig is a transmission gearbox. Artificially damaged gear teeth were introduced. The ICR technique provides a practical alternative tool for fault diagnosis. It proves to be effective in diagnosing damaged gear teeth.
Thesis (PhD)--University of Pretoria, 2011.
Mechanical and Aeronautical Engineering
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Tsai, Shiu-Ming, and 蔡旭銘. "The DSP Implementation of a Stand-alone Diagnosis System for Rotating Machinery." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/04657528857447530962.

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Abstract:

碩士
國立交通大學
機械工程研究所
86
This research concerns the on-line fault detection and isolation(FDI) system applied to the rotating machinery. The architecture of the system is mainly comprised of two parts. The first part is the feature generation. We generate the feature by using the parameter identification. The second part is the fault inference. The limit checking is exploited for inference. The signals of the rotating velocity and lateral vibration on X -axis and Y -axis are measured in this approach. The FDI system is carried out by the digital signal processor. Besides, the rotor kit is built for examining if the theory is correct. Finally, we successfully developed a stand-alone FDI system applied to the rotating machinery.

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