Relevant bibliographies by topics / Rotating Machines Diagnostic

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Rotating Machines Diagnostic'

Author: Grafiati
Published: 5 April 2025

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Journal articles on the topic "Rotating Machines Diagnostic"

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Frosini, Lucia. "Novel Diagnostic Techniques for Rotating Electrical Machines—A Review." Energies 13, no. 19 (September 27, 2020): 5066. http://dx.doi.org/10.3390/en13195066.

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This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature.
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HRANIAK, Valerii, and Oleh HRYSHCHUK. "DEVELOPMENT OF THE CONCEPT OF BUILDING DIAGNOSTIC SYSTEMS OF ROTATING ELECTRICAL MACHINES UNDER THE CONDITIONS OF LIMITED INFORMATIONALITY OF DIAGNOSTIC SIGNS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 70–77. http://dx.doi.org/10.31891/2307-5732-2022-311-4-70-77.

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The article examines the peculiarities of the construction of systems for diagnosing rotating electric machines in the real conditions of their operation. It is shown that in the specified modes of operation there is a problem of limited informativeness of input information parameters that can be used to build such systems. At the same time, an additional limiting factor that must be considered when designing and implementing such equipment is the limited possibility of intervention in the design of the electric machine, which is usually limited to the manufacturing plant. As a result of a thorough analysis of the latest research in the direction of the development of diagnostic systems for rotating electric machines, a systematization of the technological parameters of electric machines that are most suitable for use in diagnostic systems was carried out. It is shown that when choosing input parameters of diagnostic systems, it is advisable to consider their informativeness, selectivity, expressiveness and complexity of the acquisition algorithm. At the same time, it is substantiated that the choice of the optimal combination of diagnostic features cannot be considered from the point of view of superposition, since each of them will be characterized by the entropy of selectivity and severity relative to defects of different types. The expediency of choosing the type of input information of diagnostic systems based on the method of evolutionary search is shown. It is demonstrated that the mentioned method allows to more completely cover the search space than, for example, gradient optimization methods, and to obtain a solution close to the optimal one in a relatively short time (a small number of iterations). The concept and typical structural diagram of the system for diagnosing rotating electric machines based on a modified non-standard artificial neural network (ANN) and the structure of the ANN itself, which considers the current mode of operation of the electric machine during diagnosis and is characterized by high adaptability to the object of diagnosis, are proposed. An example of its hardware implementation is given.
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Gizelska, Małgorzata, Dorota Kozanecka, and Zbigniew Kozanecki. "Diagnostics of the Mechatronic Rotating System." Key Engineering Materials 588 (October 2013): 101–8. http://dx.doi.org/10.4028/www.scientific.net/kem.588.101.

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Nowadays rotating systems are equipped with diagnostic systems that are based on collecting and recording measurement data and that process a huge amount of data during the machine operation. An analysis of these data and their interpretation, as well as finding a correlation between process parameters and dynamics of the machine is a very important problem. In the paper, a concept and selected procedures of the specialized software using advanced information technology for the diagnostic system dedicated for systems of rotating machines with active magnetic bearings will be presented. It is used in the actual operation of the machine, enabling an increase of its reliability. The paper presents some selected results of control of the proper operation of the mechatronic rotating system, carried out in the automatic mode.
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Pennacchi, P., and A. Vania. "Diagnosis and Model Based Identification of a Coupling Misalignment." Shock and Vibration 12, no. 4 (2005): 293–308. http://dx.doi.org/10.1155/2005/607319.

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This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.
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Golonka, Emil, and Michał Pająk. "Selected faults of low-speed machines, analysis of diagnostic signals." MATEC Web of Conferences 351 (2021): 01025. http://dx.doi.org/10.1051/matecconf/202135101025.

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In an industrial environment, a large part of the solutions used in machine parks is based on rotating systems. Therefore, the branches of diagnostics in this area are subjected to extensive research so that they effect with more and more new solutions. This article presents the problem of selected most common faults in a low-speed machinery environment. Presented chapters define the concept of symptoms and diagnostics and define its goals. Selected issues of the analysis of diagnostic signals were also discussed.
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Khan, Muhammad Amir, Bilal Asad, Karolina Kudelina, Toomas Vaimann, and Ants Kallaste. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art." Energies 16, no. 1 (December 27, 2022): 296. http://dx.doi.org/10.3390/en16010296.

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Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.
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Gizelska, Małgorzata, Dorota Kozanecka, and Zbigniew Kozanecki. "Monitoring and Diagnostics of the Rotating System with an Active Magnetic Bearing." Solid State Phenomena 198 (March 2013): 547–52. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.547.

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In the paper, a concept and selected procedures of the specialized software using advanced information technology for the diagnostic system dedicated for systems of rotating machines with active magnetic bearings will be presented. It is used in the actual operation of the machine, enabling an increase of its reliability. The paper presents some selected results of control of the proper operation of the mechatronic rotating system, carried out in the automatic mode.
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Sule, Aliyu Hamza. "Rotating Electrical Machines: Types, Applications and Recent Advances." European Journal of Theoretical and Applied Sciences 1, no. 5 (September 1, 2023): 589–97. http://dx.doi.org/10.59324/ejtas.2023.1(5).47.

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The Rotating Electrical Machines (REMs) are classified into Motors and Generators. They powered the industrial, domestic and commercial loads. Because of their importance. This paper discussed different types of REMs, their applications and recent advances. REMs are applied in Teaching, Domestic, Mechatronics, Motorcycle, Three-wheelers, Electric Vehicle, Healthcare, Flywheel Energy Storage and Wind Energy Conversion Systems. It periscopes the advances of REMs in design, Fault diagnostic, control and condition monitoring. Its significance is to shed light on some advances made in REM.
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Kumar, Rahul R., Mauro Andriollo, Giansalvo Cirrincione, Maurizio Cirrincione, and Andrea Tortella. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors." Energies 15, no. 23 (November 25, 2022): 8938. http://dx.doi.org/10.3390/en15238938.

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This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.
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Bielawski, Piotr. "Marine Propulsion System Vibration Sensor Heads." New Trends in Production Engineering 1, no. 1 (October 1, 2018): 729–37. http://dx.doi.org/10.2478/ntpe-2018-0092.

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Abstract Vibration symptoms are the main symptoms used for diagnosing machines. This applies mainly to vibrations of non-rotating machines. Symptoms of rotating element vibrations are used in a limited scope, while mostly used are the symptoms of radial vibrations of rotating shafts. Across industries, the use of technical vibration diagnosis varies. Marine propulsion systems are poorly equipped with diagnostic equipment of that type. One of the main reasons is lack of appropriate sensors. The study presents two solutions of sensor heads. One solution applies to a sensor head built into the free end of the crankshaft of a reciprocating machine. The shaft free end sensor allows measurement of torsional and longitudinal vibration accelerations of the free end as a function of shaft rotation. The other solution refers to a sensor head built into sealed slide bearing of a straight shaft. The slide bearing head enables measurement of the eccentricity to the journal relative to the shell. Sensor heads under consideration are particularly suitable to be built in the ship’s propulsion system and integrated with the ship’s maintenance system. Sensors of the ship’s maintenance system equipped with these heads will allow the operator to draw conclusions concerning the wear margins of the propulsion engine and that of the tail shaft.
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Dissertations / Theses on the topic "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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Books on the topic "Rotating Machines Diagnostic"

1

T, Hatch Charles, and Grissom Bob, eds. Fundamentals of rotating machinery diagnostics. Minden, Nev: Bently Pressurized Bearing Press, 2002.

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Diana, G., ed. Diagnostics of Rotating Machines in Power Plants. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3.

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Carlson, David K. Artificicial [i.e. Artificial] neural networks and their applications in diagnostics of incipient faults in rotating machinery. Monterey, Calif: Naval Postgraduate School, 1991.

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CISM/IFToMM Symposium (1993 Udine, Italy). Diagnostics of rotating machines in power plants: Proceedings of the CISM/IFToMM Symposium, October 27-29, 1993, Udine, Italy. Wien: Springer-Verlag, 1994.

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Grissom, Bob, Charles T. Hatch, and Donald E. Bently. Fundamentals of Rotating Machinery Diagnostics. ASME Press, 2002. http://dx.doi.org/10.1115/1.801frm.

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Rotating Machinery and Signal Processing: Proceedings of the First Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, ... Setif, Algeria. Springer, 2018.

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Chaari, Fakher, Mohamed Haddar, and Ahmed Felkaoui. Rotating Machinery and Signal Processing: Proceedings of the First Workshop on Signal Processing Applied to Rotating Machinery Diagnostics, ... Setif, Algeria. Springer, 2018.

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Zhan, Yimin. Diagnostic models for rotating machinery subject to vibration monitoring for condition-based maintenance. 2003.

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Diana, G. Diagnostics of Rotating Machines in Power Plants: Proceedings of the CISM/IFToMM Symposium, October 27-29, 1993, Udine, Italy. Springer London, Limited, 2014.

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Diana, G. Diagnostics of Rotating Machines in Power Plants: Proceedings of the CISM-IFToMM Symposium, October 27 - 29, 1993, Udine, Italy (CISM International Centre for Mechanical Sciences). Springer, 2002.

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Book chapters on the topic "Rotating Machines Diagnostic"

1

Diana, G., A. Vania, A. Vallini, and G. A. Zanetta. "Diagnostic Techniques in Condition Monitoring." In Diagnostics of Rotating Machines in Power Plants, 343–53. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_25.

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Sisti, G., and G. Ferrari Aggradi. "Expert Diagnostic System for Gas Turbines." In Diagnostics of Rotating Machines in Power Plants, 99–109. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_7.

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Kanki, H., C. Yasuda, S. Umemura, R. Itoh, C. Miyamoto, and T. Kawaguchi. "Vibration Diagnostic Expert System for Steam Turbines." In Diagnostics of Rotating Machines in Power Plants, 25–35. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_2.

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Tanaka, M. "The Diagnostic Technologies in Power Plants in Japan." In Diagnostics of Rotating Machines in Power Plants, 199–210. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_15.

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Smalley, A. J., R. M. Baldwin, G. H. Quentin, and H. R. Simmons. "Diagnostic Technology for Combustion Turbines in Power Plants." In Diagnostics of Rotating Machines in Power Plants, 211–40. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_16.

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Matsushita, Osami, Masato Tanaka, Masao Kobayashi, Patrick Keogh, and Hiroshi Kanki. "Signal Processing for Rotor Vibration Diagnosis." In Vibrations of Rotating Machinery, 301–51. Tokyo: Springer Japan, 2019. http://dx.doi.org/10.1007/978-4-431-55453-0_10.

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Lim, G. H. "Rotating machinery noise and vibration study." In Condition Monitoring and Diagnostic Engineering Management, 120–25. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_20.

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Uhrig, S., F. Öttl, N. Augeneder, and R. Hinterholzer. "Reliable Diagnostics on Rotating Machines Using FRA." In Lecture Notes in Electrical Engineering, 738–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31676-1_70.

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Nordmann, R. "Identification Techniques in Rotordynamics." In Diagnostics of Rotating Machines in Power Plants, 1–24. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_1.

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Cavagnino, C., G. L. Lapini, M. Zippo, P. Moretti, and R. Bernante. "Experimental Verification of a Non Intrusive Method for Detecting Steam Turbine Blade Resonances." In Diagnostics of Rotating Machines in Power Plants, 137–47. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-2706-3_10.

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Conference papers on the topic "Rotating Machines Diagnostic"

1

Plötz, Sandra, Lukas Ranzinger, and Stephanie Uhrig. "Frequency Response Analysis on Rotating Machines – Model Parameterization for Different Machine Types and Performance Classes." In 2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika), 01–06. IEEE, 2024. http://dx.doi.org/10.1109/diagnostika61830.2024.10693894.

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Staubach, Christian, Stefan Reddig, and Patrick Zander. "Practical Experience with PD-Localization in Rotating Machines." In 2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/diagnostika61830.2024.10693883.

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Pattanadech, Norasage, and Siwakorn Jeenmuang. "Condition Assessment of Rotating Machine Insulation: Case Studies from Power Plants." In 2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika), 01–06. IEEE, 2024. http://dx.doi.org/10.1109/diagnostika61830.2024.10693915.

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Ranzinger, Lukas, Stephanie Uhrig, Stefan Tenbohlen, and Sandra Plötz. "Transferability of SFRA Measurements Between Rotating Machines of Different Power Class and Type." In 2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/diagnostika61830.2024.10693884.

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Zidane, Ousama, Rainer Haller, Pavel Trnka, and Hans Bärnklau. "Accelerated Life Testing for Rotating Machine Insulating Materials Exposed to High AC Voltage." In 2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika), 01–05. IEEE, 2024. http://dx.doi.org/10.1109/diagnostika61830.2024.10693924.

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Amjad, Shanzay, Muhammad Faraz, and Syed Zohaib Hassan Naqvi. "Fault Diagnosis in Rotating Machines using Vibration Signal Analysis and Machine Learning." In 2024 19th International Conference on Emerging Technologies (ICET), 1–6. IEEE, 2024. https://doi.org/10.1109/icet63392.2024.10935064.

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Pan, Haiyang, Chunan Chen, Jinde Zheng, Jinyu Tong, and Jian Chen. "Fault Diagnosis Method for Rotating Machinery Based on Multi-Task Support Matrix Machine." In 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), 1–6. IEEE, 2024. https://doi.org/10.1109/icsmd64214.2024.10920299.

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Yang, Lin, Jun Lu, Zhe Yang, Yunwei Huang, Jianyu Long, and Chuan Li. "Compound Fault Diagnosis of Rotating Machinery Using Contrastive Representation Inference." In 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 158–62. IEEE, 2024. http://dx.doi.org/10.1109/sdpc62810.2024.10707737.

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Kim, Yong Joo, and Jin Lee. "Pre-Trained Partial Discharge Evaluator for Diagnosis of Rotating Machine." In 2024 10th International Conference on Condition Monitoring and Diagnosis (CMD), 242–45. IEEE, 2024. https://doi.org/10.23919/cmd62064.2024.10766089.

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Fiori de Castro, Helio, and Natalia Tyminski. "Fault Diagnostic for Rotating Machines using Bayesian Networks." In 24th ABCM International Congress of Mechanical Engineering. ABCM, 2017. http://dx.doi.org/10.26678/abcm.cobem2017.cob17-0809.

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