Relevant bibliographies by topics / Machine Diagnostic

Academic literature on the topic 'Machine Diagnostic'

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Published: 14 March 2023

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Journal articles on the topic "Machine Diagnostic"

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Duda, Arkadiusz, and Maciej Sułowicz. "A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms." Energies 13, no. 14 (July 9, 2020): 3544. http://dx.doi.org/10.3390/en13143544.

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Non-invasive diagnostic methods for electric machines’ diagnostics, which can be used during their operation in a drive system, are needed in many branches of the production industry. For the reliable condition assessment of electric machines, especially those operating in drive systems, various tools and methods have been suggested. One diagnostic method that has not been fully recognized and documented is a diagnostic method based on zero-sequence voltage component (ZSV) applications for the condition assessment of induction machines. In this paper, the application of ZSV in induction machine diagnostics is proposed. A factor that speaks in favor of applying this signal in such diagnostics is the high sensitivity of the signal to damage occurrence, and the distinct change of extracted symptoms in the case of asymmetry. It is possible to obtain a high signal amplitude, which simplifies its processing and the elaboration of reliable diagnostic factors. This ZSV-based method is also able to be applied to big machines used in industry. Due to the saturation effects visible in the ZSV signal, new diagnostic symptoms can appear, which allows for an easier condition assessment of certain machines. The usefulness of the described diagnostic method in machine condition assessment was shown through an equivalent circuit modeling process, finite element analysis, and laboratory tests of the machine.
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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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Veselovska, Nataliia. "DEVELOPMENT OF ALGORITHMIC SUPPORT FOR PRACTICAL IMPLEMENTATION OF TESTING AND DIAGNOSTIC COMPLEX OF CNC MACHINES." Vibrations in engineering and technology, no. 1(104) (April 29, 2022): 71–80. http://dx.doi.org/10.37128/2306-8744-2022-1-9.

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CNC machines, including multi-purpose machines (product processing centers), have been widely used in connection with the creation of flexible production systems and significant progress in developing and improving the reliability of multi-purpose freely programmable, multiprocessor and small numerical program control devices (CNC) , so the development of algorithmic software for the practical implementation of the test and diagnostic complex and the use of diagnostic systems as a means of automation of information technology is one of the general directions of improving the efficiency of machine-building enterprises. However, in order to make decisions about the scope, stages and feasibility of using a particular diagnostic system for the selected object of study at a particular enterprise, it is necessary to assess its expected benefits. The use of diagnostics during operation of the machine imposes its influence on the means and methods of diagnosis, which should be convenient for use in the factory, to ensure the diagnosis process in the shortest time, to have reliable readings, especially with high requirements for product reliability. and in some cases without disrupting the mechanism, to be economically feasible. The design of the machine is adapted to the needs of diagnostics and must: have built-in devices that evaluate its parameters (pressure in the hydraulic system, temperature of energy-intensive units, accuracy of machining, speed); periodically connect to special equipment that will diagnose the basic parameters of the machine and provide data on its condition. The parameters of the technical condition (diagnostic features), which can be judged on the OD and which are diagnosed during operation of the machine, are: machine parameters that directly characterize its efficiency; damage and defects that occur during operation and lead or may lead to failure; side effects that are functionally or stochastically related to the initial parameters.
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VISHNEVSKY, A. A., I. I. ARTOBOLEVSKY, and M. L. BYKHOVSKY. "Principles of Diagnostic Machine Construction1." Acta Medica Scandinavica 176, no. 2 (April 24, 2009): 129–35. http://dx.doi.org/10.1111/j.0954-6820.1964.tb00919.x.

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Зимовець, Вікторія Ігорівна, Олександр Сергійович Приходченко, and Микита Ігорович Мироненко. "ІНФОРМАЦІЙНО-ЕКСТРЕМАЛЬНИЙ КЛАСТЕР-АНАЛІЗ ВХІДНИХ ДАНИХ ПРИ ФУНКЦІОНАЛЬНОМУ ДІАГНОСТУВАННІ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (December 25, 2019): 105–15. http://dx.doi.org/10.32620/reks.2019.4.12.

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The study aims to increase the functional efficiency of machine learning of the functional diagnosis system of a multi-rope shaft hoist through cluster analysis of diagnostic features. To achieve the goal, it was necessary to solve the following tasks: formalize the formulation of the task of information synthesis, capable of learning a functional diagnosis system, which operates in the cluster-analysis mode of diagnostic signs; to propose a categorical model and, on its basis, to develop an algorithm for information-extreme cluster analysis of diagnostic signs in the process of information-extreme machine learning of a functional diagnostic system; carry out fuzzification of input fuzzy data by optimizing the geometric parameters of hyperspherical containers of recognition classes that characterize the possible technical conditions of the diagnostic object; to develop an algorithm and implement it on the example of information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine. The object of the study is the processes of information synthesis of a functional diagnostic system capable of learning, integrated into the automated control system of a multi-rope mine hoisting machine. The subject of the study is categorical models, an information-extremal machine learning algorithm of a functional diagnostic system that operates in the cluster analysis model of diagnostic signs and constructs decision rules. The research methods are based on the ideas and methods of information-extreme intellectual data analysis technology, a theoretical-informational approach to assessing the functional effectiveness of machine learning and on the geometric approach of pattern recognition theory. As a result, the following results were obtained: a categorical model was proposed, and on its basis, an algorithm for information-extremal machine learning of the functional diagnostics system for a multi-rope mine hoist was developed and implemented, which allows you to automatically generate an input classified fuzzy training matrix, which significantly reduces time and material costs when creating incoming mathematical description. The obtained result was achieved by cluster analysis of structured vectors of diagnostic signs obtained from archival data for three recognition classes using the k-means procedure. As a criterion for optimizing machine learning parameters, we considered a modified Kullback measure in the form of a functional on the exact characteristics of diagnostic solutions and distance criteria for the proximity of recognition classes. Based on the optimal geometric parameters of the containers of recognition classes obtained during machine learning, decisive rules were constructed that allowed us to classify the vectors of diagnostic features of recognition classes with a rather high total probability of making the correct diagnostic decisions. Conclusions. The scientific novelty of the results obtained consists in the development of a new method for the information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine, which operates in the cluster analysis model, which made it possible to automatically form an input classified fuzzy training matrix with its subsequent dephasification in the process of information-extreme machine learning system.
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Nikitin, Yury, Pavol Božek, and Jozef Peterka. "Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support." Sensors 20, no. 16 (August 8, 2020): 4429. http://dx.doi.org/10.3390/s20164429.

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The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.
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Fariz Qafarov, Fariz Qafarov, Elnarə Səlimova Elnarə Səlimova, and Aybəniz Əmirova Aybəniz Əmirova. "VIBRATION PROCESSES AND THEIR RELATIONSHIP WITH DEFECTS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 11, no. 07 (November 5, 2021): 81–86. http://dx.doi.org/10.36962/pahtei1107202181.

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ABSTRACT The article is devoted to vibration diagnostics, an effective method for assessing the parameters of the mechanical state of centrifugal pumping units. The use of vibration diagnostics allows, due to early detection of malfunctions, to improve target operation, increase the turnaround time and reduce the likelihood of emergency destruction of pumping unit elements. Diagnostic signs of the presence of defects in various elements of the pumping unit are presented. During the development of diagnostic methods, the character stages of the development of injuries are selected. These stages are the formation of the injury, the accumulation of injuries, collapse, and so on. consists of stages. Dynamic forces are considered to be the main cause of vibration in machine parts. It is under the influence of dynamic forces that fatigue breaks down in machine parts. The use of vibrodiagnostics in machine parts allows to accurately assess the degree of damage to its individual nodes. This, in turn, leads to improved operating conditions. In conclusion, it should be noted that vibrodiagnostics not only detects malfunctions in machines, but also reveals the causes of its formation. Keywords: vibrodiagnostics, improvement of operational conditions, determination of defects, probability of emergency destruction, repair, assessment.
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Bartels, P. H., D. Thompson, H. G. Bartels, and R. Shoemaker. "Machine Vision System for Diagnostic Histopathology." Pathology - Research and Practice 185, no. 5 (December 1989): 635–46. http://dx.doi.org/10.1016/s0344-0338(89)80209-2.

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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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Szabó, József Zoltán. "Forgógépek üzem közbeni mozgásának próbapadi és ipari vizsgálata." Jelenkori Társadalmi és Gazdasági Folyamatok 7, no. 1-2 (January 1, 2012): 73–79. http://dx.doi.org/10.14232/jtgf.2012.1-2.73-79.

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The significance of predictive maintenance and vibration diagnostic increasing continuously in every area of industry. Vibration diagnostics delivers considerable economic benefits for machinery operation. Vibration measurement can show up mechanical problems and harmful effects which acts on the parts throughout the operating period of the machine. This paper describes an alternative method of vibration diagnostic with ,,Vibshape"moving-animation software. With this program and an vibration analyser the moving of the machines, and the mechanical, structures can be visible. This article shows the basic knowledge of vibration diagnostic with animation program, and a case study from my practice. The case study shows the moving of the machines in main mechanical problems, for example resonance and electrical problem. Across of the case study we can see the resonance problem of a supporting structure and a big blower rotor bar crack.
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Dissertations / Theses on the topic "Machine Diagnostic"

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Kříž, Petr. "Online vibrační diagnostika vřetene frézovacího stroje DATRON." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-402508.

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This master’s thesis deals with online vibration diagnostics of the milling machine spindle. It is aimed at the implementation of the complex online vibration diagnostics system to chosen milling machine, the suggestion of the vibration measurement system and the suggestion of the evaluation of changes of the technical condition of this milling machine spindle. The description of the vibration diagnostics, the description of the milling machine spindle construction and function and the suggestions for the practical application of vibration diagnostics are also parts of this thesis.
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Zhong, Binglin. "Model building and machine fault diagnosis." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340889.

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Tcheeko, Lot. "Didacticiel d'apprentissage du diagnostic d'erreurs en langage machine." Paris 6, 1990. http://www.theses.fr/1990PA066332.

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L'enseignement des bases de l'architecture des ordinateurs a des debutants pose un certain nombre de problemes techniques. Le but de cette these est de developper dans un logiciel pedagogique de simulation d'un microprocesseur, une aide personnalisee en ligne correspondant a un didacticiel. Ceci afin de guider les apprenants dans la detection et la correction de leurs erreurs de programmation. Les modeles de diagnostic dans les tutoriels intelligents sont essentiellement developpes autour des langages de haut niveau (pascal, prolog, lisp. . . ) et utilisent une representation a base de regles pour le domaine de connaissances. L'originalite de notre approche est qu'elle se situe au niveau du langage machine et propose a l'etudiant un outil d'assistance et d'auto-evaluation. Cet environnement de programmation est construit autour du simulateur micro3 qui est le support de l'apprentissage de l'architecture interne des micro-ordinateurs. Il permet de fournir une aide a la comprehension des erreurs semantiques de l'apprenant
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Raoult, Olivier Lux Augustin Mossière Jacques Demogeot Jacques. "Diagnostic de pannes des systèmes complexes." S.l. : Université Grenoble 1, 2008. http://tel.archives-ouvertes.fr/tel-00332209.

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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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Keuneke, Anne Marie. "Machine understanding of devices causal explanation of diagnostic conclusions /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487671640057361.

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Abed, Aïcha. "Contribution à l'étude et au diagnostic de la machine asynchrone." Nancy 1, 2002. http://www.theses.fr/2002NAN10020.

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Utilisée dans la plupart des entraînements électriques, la machine asynchrone tend à supplanter la machine à courant continu ainsi que la machine synchrone en raison de ses nombreuses qualités, et principalement de son faible coût et de sa robustesse. Ainsi, une réflexion générale est engagée en matière de modélisation et de diagnostic des défauts stator/rotor. Plus particulièrement, nous proposons d'étudier les défauts rotoriques (ruptures des barres au rotor). En premier temps, nous développons deux modèles de la machine asynchrone pour la simulation des ruptures de barres rotoriques. Nous présentons dans la suite trois méthodes pour la détection de ce défaut. Le principe de la détection est basé sur l'analyse spectrale du courant statorique afin de suivre l'évolution des fréquences liées au défaut. Enfin, une étude du défaut en présence d'une commande vectorielle classique est présentée ouvrant une nouvelle voie vers un diagnostic dans le cas d'une vitesse variable. Une partie expérimentale a permis de vérifier l'exactitude des résultats théoriques et a montré l'efficacité des méthodes développées
Used in the majority of the electric drives, the asynchronous machine tends to supplant the machine with D. C. Current as well as the synchronous machine because of its many qualities, and mainly of its low cost and its robustness. Thus, a general reflexion is committed in modeling and diagnostic of induction machine defects. More particularly, we propose to study the rotor defects (broken bars in the rotor). In the first time, we develop two models of the asynchronous machine for the simulation of broken bars. We present in the continuation three methods to detect this fault. The principle of detection is based on the spectral analysis of the stator current in order to follow the evolution of the frequencies which are related to the fault. Lastly, a study of the defect in the presence of a classical vector control is presented, opening a new way towards a diagnostic in the case of speed variation. An experimental part is carried out to validate the exactitude of the theoretical results and to show the effectiveness of the developed methods
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Bachir, Smaïl. "Contribution au diagnostic de la machine asynchrone par estimation paramétrique." Poitiers, 2002. http://www.theses.fr/2002POIT2306.

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Le travail de recherche présenté dans ce mémoire concerne la mise en oeuvre d'une méthodologie de diagnostic de la machine asynchrone basée sur l'estimation paramétrique. Dans une première étape, les différentes techniques de surveillance des entraînements électriques ont été recensées afin d'analyser brièvement leurs performances respectives. Une méthodologie générale de surveillance de procédés industriels est ensuite élaborée. Cette technique s'appuie sur une modélisation de la signature de défauts en associant un mode commun (modèle sain) et un mode différentiel (de défaut) et sur une méthodologie d'estimation paramétrique permettant l'adjonction de l'expertise de l'utilisateur. Une étude expérimentale avec une bobine à noyau de fer, structure de base des machines électriques, a alors permis de valider cette approche. Dans un deuxième temps, nous nous sommes intéressés à l'application de cette stratégie à la surveillance des défauts statoriques et rotoriques de la machine asynchrone. Exprimé dans le repère diphasé de Park, un modèle original de la machine asynchrone en défaut de court-circuit de spires statoriques et de rupture de barres rotoriques est présenté. La procédure de diagnostic, appliquée sur l'ensemble des modèles de défaut proposés, a été validée expérimentalement. En parallèle, nous nous sommes attachés à améliorer et à généraliser cette approche, en minimisant le nombre de capteurs nécessaires au diagnostic afin de simplifier et de fiabiliser cette méthodologie, et en adaptant des algorithmes récursifs pour une détection en temps réel des défauts.
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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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Hrbáček, Vlastimil. "Návrh provozních mezí pro diagnostický systém obráběcího stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417438.

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The Master's thesis deals with online vibration diagnostics of a milling center. It contains a methodology to determine the vibration operating limits and to detect machine of condition monitoring. The thesis also contains a general description of maintenance and technical diagnostics with a focus on vibrodiagnostics. The main part of the thesis is to solve the problem of determining the operating limits of spindle vibration depending on the production program. The essence of the solution is to determine vibration limits for individual milling tools.
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Books on the topic "Machine Diagnostic"

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Naidenova, Xenia, and Viktor Shagalov. Diagnostic test approaches to machine learning and commonsense reasoning systems. Hershey, PA: Information Science Reference, 2013.

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Miller, Richard Kendall. Survey on X-ray machine vision and compute [sic] tomography. Madison, GA: Future Technology Surveys, 1989.

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Machine learning in computer-aided diagnosis: Medical imaging intelligence and analysis. Hershey: Medical Information Science Reference, 2011.

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1970-, Gonzalez Fabio A., and Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.

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1970-, Gonzalez Fabio A., and Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.

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MLMI 2010 (2010 Beijing, China). Machine learning in medical imaging: First International Workshop, MLMI 2010, held in conjunction with MICCAI 2010, Beijing, China, September 20, 2010 : proceedings. Berlin: Springer, 2010.

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Sibikin, Mihail, A. N. Chernenko, and Yuriya Voronkin. Technological equipment. Metal cutting machines. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1061257.

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The textbook discusses and describes the technological equipment of machine tool construction: metalworking machines; standard mechanisms and devices for machine tools; purpose, device, kinematics, adjustment of machines of various groups and types; multipurpose and aggregate machines; precision equipment; automatic lines; flexible production module; flexible production system; testing of machines; indicators of the technical level and reliability of technological equipment; diagnostics of machine systems. Meets the requirements of the federal state educational standards of secondary vocational education of the latest generation. For students of secondary vocational education institutions. It can be useful for professional training of technicians and craftsmen.
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Trigeassou, Jean-Claude. Electrical Machines Diagnosis. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2011. http://dx.doi.org/10.1002/9781118601662.

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Electrical machines diagnosis. London: ISTE, 2011.

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Williams, J. Hywel. Condition-based maintenance and machine diagnostics. London: Chapman & Hall, 1994.

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Book chapters on the topic "Machine Diagnostic"

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Smith, Graham T. "Telescoping Ballbars and Other Diagnostic Instrumentation." In Machine Tool Metrology, 345–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25109-7_4.

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Strobel, Michael, and Hans-Werner Stedtfeld. "Machine Evaluation of Laxity." In Diagnostic Evaluation of the Knee, 258–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74397-9_9.

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Kukar, Matjaž, and Ciril Grošelj. "Machine Learning in Stepwise Diagnostic Process." In Artificial Intelligence in Medicine, 315–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48720-4_34.

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Davies, A., and J. H. Williams. "The condition monitoring of machine tools." In Condition Monitoring and Diagnostic Engineering Management, 44–48. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_8.

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Suzuki, Kenji. "Computerized Detection of Lesions in Diagnostic Images." In Machine Learning in Radiation Oncology, 101–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18305-3_7.

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Bachschmid, Nicolò. "Some Examples of Incomplete Diagnostic Analyses of Industrial Machinery." In Mechanisms and Machine Science, 191–206. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99268-6_14.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Logistic Regression for Assessing Novel Diagnostic Tests Against Control." In Machine Learning in Medicine, 45–52. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6886-4_6.

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Sun, Delin. "Development of New Diagnostic Techniques – Machine Learning." In Advances in Experimental Medicine and Biology, 203–15. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5562-1_10.

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Luelf, G., and R. Vogel. "Vibration analysis as tool for computerised machine monitoring." In Condition Monitoring and Diagnostic Engineering Management, 126–31. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_21.

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Guo, Qianjin, Haibin Yu, and Aidong Xu. "A New Intelligent Diagnostic Method for Machine Maintenance." In Advances in Machine Learning and Cybernetics, 760–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_79.

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Conference papers on the topic "Machine Diagnostic"

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Kachin, Oleg, and Sergey Kachin. "Diagnostic of moving machine parts." In 2012 7th International Forum on Strategic Technology (IFOST). IEEE, 2012. http://dx.doi.org/10.1109/ifost.2012.6357703.

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Janda, M., O. Vitek, and M. Skalka. "Noise diagnostic of induction machine." In 2010 XIX International Conference on Electrical Machines (ICEM). IEEE, 2010. http://dx.doi.org/10.1109/icelmach.2010.5608036.

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Pan, Min-Chun, and Po-Ching Li. "Remote online machine fault diagnostic system." In NDE for Health Monitoring and Diagnostics, edited by Tribikram Kundu. SPIE, 2004. http://dx.doi.org/10.1117/12.537722.

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Singh, Ajay, Anand Shukla, and Suryansh Purwar. "Leveraging Machine Learning and Interactive Voice Interface for Automated Production Monitoring and Diagnostic." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210475-ms.

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Abstract Automated production monitoring and diagnostics is becoming essential for oil producers to achieve operational efficiency. In this work a combination of unsupervised and supervised machine-learning (ML) models are proposed and were integrated with interactive voice interface so that production diagnostic reports can be generated by using interactive session with chatbot. To achieve this, current work proposes an integration of ML models and chatbot in the cloud native environment and presents a case study using data from hundreds of wells supported on plunger lift system. Within ML framework data preprocessing and principle component analysis (PCA) was performed. The purpose of PCA was to identify principle components (PCs) and the projection production rate data over few dominating PCs and generate 2D or 3D plots which can be used to cluster wells based on production trends and relative performance. Then using daily production data, a regression tree analysis (per well) was performed to predict production rate for dominating phase for production. Regression tree generated if-else type rules which were used for production diagnostics. Further, using early few months of time series data for production, pressure and artificial lift data, another PCA model was trained and contribution chart (per well) were developed to identify which are the most contributing variables towards the change in the production such as increase or decrease in production rate. Finally, to enhance end user experience, a cloud native chatbot leveraging cloud services was configured to perform all steps involved in ML framework in serverless compute environment. The chatbot was built to answer frequently asked production monitoring and diagnostics questions such as "provide me a list of poor performing well" etc. The proposed framework was applied to wells supported on plunger lift and PCA revealed that that four PCs were enough to capture most dominating production modes and first 3 PC described 96.2% of variance. The diagnostic charts were built utilizing 2D and 3D diagrams using projection of gas production rate over first 3 PCs. This was found visually extremely useful to identify which well or group of wells were not performing as expected when compared to rest of the wells. Just by looking 2D plot about 10% wells were found with significant decrease while about 15% were found moderate decrease in production rate. Once identified poorly performing wells regression tree analysis was automatically generated along with the contribution charts for all variables. Couple of case studies were presented using two different wells with contrast production trend and it was demonstrated that the present workflow was able to identify relative behavior of those wells and presented detailed diagnostics using regression tree analysis and contribution charts. Overall, diagnostic charts were able to identify how to calibrate plunger count, plunger velocity, trip time etc. for improved production and forecasted up to 30% production improvement for poor producing wells. Finally, the results were tested out with chatbot. The chatbot model was deployed using web user interface and to answer production diagnostics related questions, chatbot utilized serverless compute to run ML models on the cloud. The output such as generated diagnostic charts and list of well etc. were prepared as user asked the questions and relevant analysis was presented to end user within a fraction of second. This can reduce time taken by well diagnostic analysis by 80%
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Xue-Nong Zhang. "Formal analysis of diagnostic notions." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359553.

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Hu, Nian-Ze, Chih-Hui Simon Su, Cihun-Siyong Alex Gong, Cheng-Jung Lee, Yong-Sheng Chen, Ching-Hsiang Yang, Ching-Ying Yeh, Zheng-Han Shi, and Jieh-Tsyr Chuang. "Machine learning approach for robot diagnostic system." In 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE). IEEE, 2019. http://dx.doi.org/10.1109/ecice47484.2019.8942793.

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Qureshi, Fayyaz Karim, and Abdelhady A. Hady Mohamed. "Advanced Analytics and Diagnostic Rules Automatically Notify Operators About Developing Failures in Rotating and Reciprocating Machines." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211244-ms.

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Abstract With the paradigm shift towards digitalization, Operators and service providers are inclined to use technologies that can optimize efforts from workforce by providing meaningful information rather than ‘just’ data, transition subject matter knowledge into machines rather than limiting to people, deploy machine learning techniques to improve systems and leverage this big data to serve on wide scale. Historically, condition monitoring knowledge has primarily been people-centric and Reliability personnel have to spend hours in front of screen reviewing terabytes of data. Unfortunately, most of the time is spent to find problems rather than finding solutions. Need of the hour is to define automated mechanisms for triggering alerts pointing towards developing malfunctions for which systems are created with embedded knowledge to run the data through pre-configured diagnostic rules and analytics. Through these online systems, operators are able to receive meaningful actionable information about the issue and its source. These analytics are widespread across machinery, auxiliary and process domains. Through this automated diagnostics platform, Data-driven insights can be generated for machine condition monitoring through advanced rule-building and data-mapping capabilities. In addition to packaged algorithms of known failure signatures, users can also create custom rules that help to capture, disseminate, and leverage knowledge of equipment, processes, and business solutions. For turbomachinery, trending of process parameters, bearing temperature and overall vibration have been used for decades to monitor condition of assets, whereas knowledgeable diagnostic personnel are required to review dynamic data like orbit shape, vibration precession, along with other attributes together to really monitor condition of machine. Now meaningful information from dynamic data can be digitized and attributes can be used in rule logics for automated diagnosis of typical malfunctions like unbalance, misalignment, rubbing, fluid induced instability, rotor bow etc. For reciprocating compressors, automated diagnosis of typical malfunctions like pressure packing leak, valve failures, crosshead pin / frame overloading, debris/liquid ingestion, auxiliary systems (lube oil, cylinder cooling system, unloader etc.) failures and several process related issues can be realized. In this paper, case studies will be demonstrated where users were able to capitalize these systems to identify some of above stated malfunctions and save their assets from expensive secondary repercussions. An operational analytics software will be demonstrated in detail with elaboration on built-in library of pre-packaged algorithms. A primary consideration is maximizing return-on-investment and minimizing payback period. Through use case studies, it will be further demonstrated on how the users were able to identify anomalies and relish 100% payback in less than 2 months of deployment.
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Leung, Jacko T., and Peter W. Tse. "Smart Asset Maintenance System for Machine Fault Diagnosis: Its Effectiveness, Methodology, and Applications." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84300.

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Maintenance is essential in all kinds of machines. In past, the machine operators would recognize the machine condition by touching the machine or hearing the machine operating sound. However, this is too subjective and not effective for inexperienced operators. In fact, most modern machineries are so complex that many components may run together, making the operator impossible to distinguish the difference between a normal and anomalous machine. Although more scientific fault diagnostic systems are available, they are expensive and difficult to use without comprehensive learning. Therefore, there is a need from industry to have an economy and efficient machine fault diagnostic system. The occurrence of fault must be identified as early as possible to avoid fatal breakdown of machines. The aim of developing the Smart Asset Maintenance System (SAMS) is to provide a portable and comprehensive but low-cost and simple-to-use solution for the industry to perform equipment maintenance effectively.
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Liu, Yongbin, Ruqiang Yan, and Robert X. Gao. "A Nonlinear Time Series Analysis Method for Health Monitoring of Rolling Bearings." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4118.

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This paper presents a nonlinear time series analysis method for rotating machine damage detection and diagnostics. Specifically, the permutation entropy is investigated as a statistical measure for signal characterization. Through space reconstruction, the permutation entropy describes the complexity of the time series measured on a physical system, and takes its non-linear behavior into account. By identifying changes in the vibration signals measured on rotating machines, which are typical precursors of defect occurrence, permutation entropy can serve as a diagnostic tool. Experiments on a custom-designed gearbox system have confirmed its effectiveness for machine structural health monitoring applications.
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Masalimov, Kamil Adipovich. "A machine learning based approach to autogenerate diagnostic models for CNC machines." In ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3324884.3418915.

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Reports on the topic "Machine Diagnostic"

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Liu, Xiaopei, Dan Liu, and Cong’e Tan. Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0133.

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Review question / Objective: The invasive liver biopsy is the gold standard for the diagnosis of liver cirrhosis. Other non-invasive diagnostic approaches, have been used as alternatives to liver biopsy, however, these methods cannot identify the pathological grade of the lesion. Recently, studies have shown that gut microbiome-based machine learning can be used as a non-invasive diagnostic approach for liver cirrhosis or fibrosis, while it lacks evidence-based support. Therefore, we performed this systematic review and meta-analysis to evaluate its predictive diagnostic value in liver cirrhosis or fibrosis. Condition being studied: Liver fibrosis and cirrhosis. Liver fibrosis refers to excessive deposition of liver fibrous tissue caused by various pathogenic factors, such as hepatitis virus, alcohol, and drug-induced chemical injury. Continuous progression of liver fibrosis can lead to liver cirrhosis.
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Bruckner, Daniel. ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada605112.

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Xie, Bin. DiagSoftfailure: Automated Soft-Failure Diagnostic Tool Using Machine Learning for Network Users. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1575995.

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Guo, Longfei, Zhilei Cui, Jing Huang, Loh Wei Ping, and Shazlin Shaharudin. Applications of machine learning to the predictive and diagnostic capabilities of ACL injuries in athletes: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0045.

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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental repairs. Dental arches in detachable prosthodontics may be categorized using Convolutional neural networks (CNN). By properly positioning the teeth, machine learning in CAD/CAM software can reestablish healthy inter-maxillary connections. AI may assist with accurate color matching in challenging cosmetic scenarios that include a single central incisor or many front teeth. Intraoral detectors can identify implant placements in implant prosthodontics and instantly input them into CAD software. The design and execution of dental implants could potentially be improved by utilizing AI.
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Alharbi, Shuaa S., and Haifa F. Alhasson. Toward the Identification of Applications of Artificial Intelligence for Dental Image Detection: Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0023.

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Review question / Objective: The purpose of this systematic review is to understand and compare the current applications of machine learning in the care of dental patients. This will enable us to assess their diagnostic and prognostic accuracy. As part of the study, we will identify areas of development for ML applications in the dental care field. In addition, we will suggest improvements to research methodology that will facilitate the implementation of ML technologies in services and improve clinical treatment guidelines based on the results of future studies. Condition being studied: This study rationally focused on reviewing the current state of Artificial Intelligence (AI) in dentistry and state-of-the-art applications, including the recognition of teeth cavities, filled teeth, crown predictions, oral surgery, and endodontic therapy.
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Reifman, J., G. E. Graham, T. Y. C. Wei, K. R. Brown, and R. Y. Chin. Flexible human machine interface for process diagnostics. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/224751.

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Ehiabhi, Jolly, and Haifeng Wang. A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0003.

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Review question / Objective: A systematic review of Mental health diagnosis/prognoses of mental disorders using Machine Learning techniques with information from biometric signals. A review of the trend and status of these ML techniques in mental health diagnosis and an investigation of how these signals are used to help increase the efficiency of mental health disease diagnosis. Using Machine learning techniques to classify mental health diseases as against using only expert knowledge for diagnosis. Feature Extraction from signal gotten from biometric signals that help classify sleep disorders. Rationale: To review the application of ML techniques on multimodal and multichannel PSG datasets got from biosensors typically used in the Hospital. To help professionals grasp the steps of using machine learning to classify mental health diseases.
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Howard, Marylesa. Health Assessment and Performance Monitoring of Large Machine Diagnostics. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1877017.

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Edmonds, P. H., S. S. Medley, and K. M. Young. TPX diagnostics for tokamak operation, plasma control and machine protection. Office of Scientific and Technical Information (OSTI), August 1995. http://dx.doi.org/10.2172/100240.

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