Log in

Relevant bibliographies by topics / Electric motor fault diagnosis

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers

Academic literature on the topic 'Electric motor fault diagnosis'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Electric motor fault diagnosis.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Electric motor fault diagnosis"

1

Ciupitu, Liviu, Andrei Tudor, Doru Turcan, and Daniel Sandor. "Vibration Diagnosis of Electric Motor’s Bearings." Advanced Materials Research 463-464 (February 2012): 1725–28. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.1725.

Full text

Abstract:

Recently technologies like vibration monitoring or acoustic measurement help the maintenance to improve the OEE (overall equipment effectiveness) factor. For example SKF company uses for fault detection vibration and temperature sensors and vibration signal processing techniques that differentiate between normal machinery process vibrations and abnormal vibrations caused by machinery faults. The fault can be eliminated or monitored until maintenance and repairs can be organized in a cost-effective way. The type of pattern with frosted or fluted features on the bearing inner or outer race it's often found on the electrical motors bearings due to electric discharge inside motor. This defect can decrease the asset life time from months to days and could lead to catastrophic defects.

APA, Harvard, Vancouver, ISO, and other styles

2

Xu, Xiaowei, Xue Qiao, Nan Zhang, Jingyi Feng, and Xiaoqing Wang. "Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles." Advances in Mechanical Engineering 12, no. 7 (July 2020): 168781402094432. http://dx.doi.org/10.1177/1687814020944323.

Full text

Abstract:

Permanent magnet synchronous motors are the main power output components of electric vehicles. Once a failure occurs, it will affect the vehicle’s power, stability, and safety. While as a complex field-circuit coupling system composed of machine-electric-magnetic-thermal, the permanent magnet synchronous motor of electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency, and communication characteristics make it difficult to diagnose faults. Based on the research of a list of related references, this article reviews the methods of intelligent fault diagnosis for electric vehicle permanent magnet synchronous motors. The research status and development trend of fault diagnosis are analyzed. It provides theoretical basis for motor fault diagnosis and health management in multi-variable working conditions and multi-physics environment.

APA, Harvard, Vancouver, ISO, and other styles

3

Ribeiro Junior, Ronny Francis, Isac Antônio dos Santos Areias, and Guilherme Ferreira Gomes. "Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types." Sensor Review 41, no. 3 (July 9, 2021): 311–19. http://dx.doi.org/10.1108/sr-02-2021-0052.

Full text

Abstract:

Purpose Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives. Design/methodology/approach This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed. Findings Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition. Originality/value The proposed methodology is verified through a real experimental setup.

APA, Harvard, Vancouver, ISO, and other styles

4

Wahab, Abbas A., N. Fatimah Abdullah, and M. A. H. Rasid. "Mechanical Fault Detection on Electrical Machine: Thermal Analysis of Small Brushed DC Motor with Faulty Bearing." MATEC Web of Conferences 225 (2018): 05012. http://dx.doi.org/10.1051/matecconf/201822505012.

Full text

Abstract:

Direct current motors (DC motor) are used in the small electric devices commonly. DC motor are cheap and easy to install, thus their popularity. Despite the popularity, faults occur which make diagnosis and detection of faults very important. It avoids financial loss and unexpected shutdown operation causes by these faults. This paper presents an analysis of temperature profile of the much famous small Brushed DC motor with a faulty bearing. The temperature data of healthy DC motor and DC motor with faulty bearing were measured by thermocouple and recorded using data logger in real time until steady state temperature, under different load. The analysis on the steady state temperature allow to conclude that bearing fault can clearly be recognised through characteristics temperature difference with a healthy motor.

APA, Harvard, Vancouver, ISO, and other styles

5

Rohan, Ali, Izaz Raouf, and Heung Soo Kim. "Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM)." Sensors 20, no. 23 (November 30, 2020): 6845. http://dx.doi.org/10.3390/s20236845.

Full text

Abstract:

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%.

APA, Harvard, Vancouver, ISO, and other styles

6

Zhang, Mingming, Jiangtian Yang, and Zhang Zhang. "Locomotive Gear Fault Diagnosis Based on Wavelet Bispectrum of Motor Current." Shock and Vibration 2021 (July 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5554777.

Full text

Abstract:

The motor current signature analysis (MCSA) provides a nondestructive method for gear fault detection. The motor current in the faulty gear system not only involves the frequency information related to the fault but also the electric supply frequency and gear meshing-related frequency, which not only contaminates the fault characteristics but also increases the difficulty of fault extraction. To extract the fault characteristic frequency effectively, an innovative method based on the wavelet bispectrum (WB) is proposed. Bispectrum is an effective tool for identifying the fault-related quadratic phase coupling (QPC). However, it requires a large amount of data averaging, which is not suitable for short data analysis. In this paper, the wavelet bispectrum is introduced to motor current analysis and the problem of QPC extraction under variable speed conditions is preliminarily solved. Furthermore, a fault diagnostic approach for locomotive gears using the wavelet bispectrum and wavelet bispectral entropy is suggested. The presented method was effectively applied to the locomotive online running operations, and faults of the drive gear were successfully diagnosed.

APA, Harvard, Vancouver, ISO, and other styles

7

Glowacz, A., W. Glowacz, Z. Glowacz, J. Kozik, M. Gutten, D. Korenciak, Z. F. Khan, M. Irfan, and E. Carletti. "Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers." Archives of Metallurgy and Materials 62, no. 4 (December 1, 2017): 2413–19. http://dx.doi.org/10.1515/amm-2017-0355.

Full text

Abstract:

AbstractA degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies – Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

APA, Harvard, Vancouver, ISO, and other styles

8

Zhao, Chunheng, Yi Li, Matthew Wessner, Chinmay Rathod, and Pierluigi Pisu. "Support-Vector Machine Approach for Robust Fault Diagnosis of Electric Vehicle Permanent Magnet Synchronous Motor." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1291.

Full text

Abstract:

Permanent magnet synchronous motor (PMSM) is a leading technology for electric vehicles (EVs) and other high-performance industrial applications. These challenging applications demand robust fault diagnosis schemes, but conventional strategies based on models, system knowledge, and signal transformation have limitations that degrade the agility of diagnosing faults. These methods require extremely detailed design and consideration to remain robust against noise and disturbances in the actual application. Recent advancements in artificial intelligence and machine learning have proven to be promising next-generation solutions for fault diagnosis. In this paper, a support-vector machine (SVM) utilizing sparse representation is developed to perform sensor fault diagnosis of a PMSM. A simulation model of the pertinent PMSM drive system for automotive applications is used to generate a set of labelled training example sets that the SVM uses to determine margins between normal and faulty operating conditions. The PMSM model includes input as a torque reference profile and disturbance as a constant road grade, against both of which faults must be detectable. Even with limited training, the SVM classifier developed in this paper is capable of diagnosing faults with a high degree of accuracy, suggesting that such methods are feasible for the demanding fault diagnosis challenge in PMSM.

APA, Harvard, Vancouver, ISO, and other styles

9

Altaf, Saud, Muhammad Waseem Soomro, and Mirza Sajid Mehmood. "Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique." Modelling and Simulation in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/1292190.

Full text

Abstract:

In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature Analysis (MCSA) method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN) learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

APA, Harvard, Vancouver, ISO, and other styles

10

Kim, Kyusung, and Alexander G. Parlos. "Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis." Journal of Dynamic Systems, Measurement, and Control 125, no. 1 (March 1, 2003): 80–95. http://dx.doi.org/10.1115/1.1543550.

Full text

Abstract:

Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance, and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence of equipment owners in this new technology. In this paper, a model-based fault diagnosis system recently proposed by the authors for induction motors is experimentally compared for fault detection and false alarm performance with a more traditional signal-based motor fault estimator. In addition to the nameplate information required for the initial set-up, the proposed model-based fault diagnosis system uses measured motor terminal currents and voltages, and motor speed. The motor model embedded in the diagnosis system is empirically obtained using dynamic recurrent neural networks, and the resulting residuals are processed using wavelet packet decomposition. The effectiveness of the model-based diagnosis system in detecting the most widely encountered motor electrical and mechanical faults, while minimizing the impact of false alarms resulting from power supply and load variations, is demonstrated through extensive testing with staged motor faults. The model-based fault diagnosis system is scalable to motors of different power ratings and it has been successfully tested with fault data from 2.2kW,373kW, and 597kW induction motors.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Electric motor fault diagnosis"

1

Cameron, James R. "Vibration and current monitoring for on-line detection of air-gap eccentricity in induction motors." Thesis, Robert Gordon University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328784.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Akin, Bilal. "Low-cost motor drive embedded fault diagnosis systems." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1488.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Sekar, Booma Devi. "Hybrid intelligent technology based fault diagnosis system for squirrel cage induction motor." Thesis, University of Macau, 2007. http://umaclib3.umac.mo/record=b1678023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Tugsal, Umut. "FAULT DIAGNOSIS OF ELECTRONIC FUEL CONTROL (EFC) VALVES VIA DYNAMIC PERFORMANCE TEST METHOD." ProQuest, 2009. http://hdl.handle.net/1805/2094.

Full text

Abstract:

Indiana University-Purdue University Indianapolis (IUPUI)
Electronic Fuel Control (EFC) valve regulates fuel flow to the injector fuel supply line in the Cummins Pressure Time (PT) fuel system. The EFC system controls the fuel flow by means of a variable orifice that is electrically actuated. The supplier of the EFC valves inspects all parts before they are sent out. Their inspection test results provide a characteristic curve which shows the relationship between pressure and current provided to the EFC valve. This curve documents the steady state characteristics of the valve but does not adequately capture its dynamic response. A dynamic test procedure is developed in order to evaluate the performance of the EFC valves. The test itself helps to understand the effects that proposed design changes will have on the stability of the overall engine system. A by product of this test is the ability to evaluate returned EFC valves that have experienced stability issues. The test determines whether an EFC valve is faulted or not before it goes out to prime time use. The characteristics of a good valve and bad valve can be observed after the dynamic test. In this thesis, a mathematical model has been combined with experimental research to investigate and understand the behavior of the characteristics of different types of EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. System Identification has been addressed to determine the transfer functions of the different types of EFC valves that were experimented. Methods have been used both in frequency domain as well as time domain. Also, based on the characteristic patterns exhibited by the EFC valves, fuzzy logic has been implemented for the use of pattern classification.

APA, Harvard, Vancouver, ISO, and other styles

5

Cheng, Siwei. "Utilizing the connected power electronic converter for improved condition monitoring of induction motors and claw-pole generators." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43638.

Full text

Abstract:

This dissertation proposes several simple, robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters and claw-pole generators with built-in rectifiers. While the flexible energy forms synthesized by power electronic converters greatly enhance the performance and expand the operating region of induction motors and claw-pole generators, they also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. In this dissertation, special characteristics of the connected closed-loop inverter and rectifier have been thoroughly analyzed, with particular interest in their impact on fault behaviors of the induction motor and the claw-pole generator. Based on the findings obtained from the theoretical and experimental analysis, several sensorless thermal, mechanical, and insulation monitoring methods are proposed by smartly utilizing special features and capabilities of the connected power electronic converter. A simple and sensitive stator turn-fault detector is proposed for induction motors fed by closed-loop inverter. In addition, a stator thermal monitoring method based on active DC current injection and direct voltage estimation is also proposed to prevent the closed-loop controlled induction motors from thermally overloading. The performance of both methods is demonstrated by extensive experimental results. Methods to detect serpentine belt slip, serpentine belt defect, rotor eccentricity have been proposed for claw-pole generators using only the available electric sensor information. Methods to detect and protect stator turn faults in claw-pole generators are also presented in this dissertation. Lastly, a novel method to detect the generalized bearing roughness fault is proposed. All the proposed condition monitoring techniques have been validated by experimental results.

APA, Harvard, Vancouver, ISO, and other styles

6

Moosavi, Anchehpoli Seyed Saeid. "Analysis and diagnosis of faults in the PMSM drivetrains for series hybrid electrical vehicles (SHEVs)." Thesis, Belfort-Montbéliard, 2013. http://www.theses.fr/2013BELF0224/document.

Full text

Abstract:

L'intérêt pour les véhicules électriques ne cesse de croitre au sein de la société contemporaine compte tenu de ses nombreuses interrogations sur l’environnement et la dépendance énergétique. Dans ce travail de thèse, nous essayons d’améliorer l’acceptabtabilité sociétale du véhicule électrique en essayant de faire avancer la recherche sur le diagnostique des défauts d’une chaine de traction électrique. Les résultats escomptés devraient permettre à terme d’améliorer la fiabilité et la durabilité de ces systèmes.Nous commençons par une revue des problèmes des défauts déjà apparus dans les véhicules hybrides séries qui disposent de l’architecture la plus proche du véhicule électrique. Une étude approfondie sur le diagnostic des défauts d’un convertisseur de puissance statique (AC-DC) ainsi que celle du moteur synchrone à aimants permanents est menée. Quatre types de défauts majeurs ont été répertoriés concernant le moteur (court-circuit au stator, démagnétisation, excentricité du rotor et défaut des roulements). Au niveau du convertisseur, nous avons considéré le défaut d’ouverture des interrupteurs. Afin d’être dans les mêmes conditions d’utilisation réelle, nous avons effectué des tests expérimentaux à vitesse et charge variables. Ce travail est basé aussi bien sur l’expérimentation que sur la modélisation. Comme par exemple, la méthode des éléments finis pour l’étude de la démagnétisation de la machine. De même, l’essai en court-circuit du stator du moteur en présence d’un contrôle vectoriel.Afin de réaliser un diagnostic en ligne des défauts, nous avons développé un modèle basé sur les réseaux de neurones. L’apprentissage de ce réseau de neurone a été effectué sur la base des résultats expérimentaux et de simulations, que nous avons réalisées. Le réseau de neurones est capable d'assimiler beaucoup de données. Ceci nous permet de classifier les défauts en termes de sévérité et de les localiser. Il permet ainsi d'évaluer le degré de performance de la chaine de traction électrique en ligne en présence des défauts et nous renseigner ainsi sur l'état de santé du système. Ces résultats devraient aboutir à l’élaboration d’une stratégie de contrôle tolérant aux défauts auto-reconfigurable pour prendre en compte les modes dégradés permettant une continuité de service du véhicule ce qui améliorera sa disponibilité
The interest in the electric vehicles rose recently due both to environmental questions and to energetic dependence of the contemporary society. Accordingly, it is necessary to study and implement in these vehicle fault diagnosis systems which enable them to be more reliable and safe enhancing its sustainability. In this work after a review on problem of faults in the drivetrain of series hybrid electric vehicles (SHEV), a deep investigation on fault diagnosis of AC-DC power converter and permanent magnet synchronous motor (PMSM) have been done as two important parts of traction chains in SHEVs. In other major part of this work, four types of faults (stator winding inter turn short circuit, demagnetization, eccentricity ant bearing faults) of a PMSM have been studied. Inter turn short circuit of stator winding of PMSM in different speeds and loads has been considered to identify fault feature in all operation aspects, as it is expected by electric vehicle application. Experimental results aiming short circuits, bearing and eccentricity fault detection has been presented. Analytical and finite element method (FEM) aiming demagnetization fault investigation has been developed. The AC-DC converter switches are generally exposed to the possibility of outbreak open phase faults because of troubles of the switching devices. This work proposes a robust and efficient identification method for data acquisition selection aiming fault analysis and detection. Two new patterns under AC-DC converter failure are identified and presented. To achieve this goal, four different level of switches fault are considered on the basis of both simulation and experimental results. For accuracy needs of the identified pattern for SHEV application, several parameters have been considered namely: capacitor size changes, load and speed variations. On the basis of the developed fault sensitive models above, an ANN based fault detection, diagnosis strategy and the related algorithm have been developed to show the way of using the identified patterns in the supervision and the diagnosis of the PMSM drivetrain of SHEVs. ANN method have been used to develop three diagnosis based models for : the vector controlled PMSM under inter turn short circuit, the AC/DC power converter under an open phase fault and also the PMSM under unbalanced voltage caused by open phase DC/AC inverter. These models allow supervising the main components of the PMSM drivetrains used to propel the SHEV. The ANN advantages of ability to include a lot of data mad possible to classify the faults in terms of their type and severity. This allows estimating the performance degree of that drivetrains during faulty conditions through the parameter state of health (SOH). The latter can be used in a global control strategy of PMSM control in degraded mode in which the control is auto-adjusted when a defect occurs on the system. The goal is to ensure a continuity of service of the SHEV in faulty conditions to improve its reliability

APA, Harvard, Vancouver, ISO, and other styles

7

Wu, Long. "Separating Load Torque Oscillation and Rotor Faults in Stator Current Based-Induction Motor Condition Monitoring." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14545.

Full text

Abstract:

Stator current spectral analysis techniques are usually used to detect rotor faults in induction machines. Magnetic field anomalies in the airgap due to the rotor faults result in characteristic side-band harmonic components in the stator current spectrum, which can be measured as rotor fault signatures. A position-varying load torque oscillation at multiples of the rotational speed, however, has exactly the same effect. Stator current harmonics due to a load torque oscillation often obscure and even overwhelm rotor eccentricity fault detection since the magnitude of load oscillation induced harmonics is usually much larger. Although previous research has suggested some methods to differentiate between these two effects, most of them rely heavily on the accurate estimation of motor parameters. The objective of this research is to develop a far more practical and computationally efficient method to detect rotor faults effectively in the presence of a load torque oscillation. A significant advantage of the proposed scheme is that it does not need any knowledge of motor parameters. The normalized negative sequence information induced by a mixed rotor eccentricity in the stator current or terminal voltage space vector spectra, serves as a reliable rotor fault indicator to eliminate load oscillation effects. Detailed airgap magnetic field analysis for an eccentric motor is performed and all machine inductance matrices as well as their derivatives are reformulated accordingly. Careful observation of these inductance matrices provides a fundamental understanding of motor operation characteristics under a fault condition. Simulation results based on both induction motor dynamic model and Maxwell 2D Finite Element Model demonstrate clearly the existence of the predicted rotor fault indicator. Extensive experimental results also validate the effectiveness and feasibility of the proposed detection scheme.

APA, Harvard, Vancouver, ISO, and other styles

8

Urresty, Betancourt Julio César. "Electrical and magnetic faults diagnosis in permanent magnet synchronous motors." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/101505.

Full text

Abstract:

Permanent magnet synchronous motors (PMSMs) are an alternative in critical applications where high-speed operation, compactness and high efficiency are required. In these applications it is highly desired to dispose of an on-line, reliable and cost-effective fault diagnosis method. Fault prediction and diagnosis allows increasing electric machines performance and raising their lifespan, thus reducing maintenance costs, while ensuring optimum reliability, safe operation and timely maintenance. Consequently this thesis is dedicated to the diagnosis of magnetic and electrical faults in PMSMs. As a first step, the behavior of a healthy machine is studied, and with this aim a new 2D finite element method (FEM) modelbased system for analyzing surface-mounted PSMSs with skewed rotor magnets is proposed. It is based on generating a geometric equivalent non-skewed permanent magnet distribution which accounts for the skewed distribution of the practical rotor, thus avoiding 3D geometries and greatly reducing the computational burden of the problem. To diagnose demagnetization faults, this thesis proposes an on-line methodology based on monitoring the zero-sequence voltage component (ZSVC). Attributes of the proposed method include simplicity, very low computational burden and high sensibility when compared with the well known stator currents analysis method. A simple expression of the ZSVC is deduced, which can be used as a fault indicator parameter. Furthermore, mechanical effects arising from demagnetization faults are studied. These effects are analyzed by means of FEM simulations and experimental tests based on direct measurements of the shaft trajectory through self-mixing interferometry. For that purpose two perpendicular laser diodes are used to measure displacements in both X and Y axes. Laser measurements proved that demagnetization faults may induce a quantifiable deviation of the rotor trajectory. In the case of electrical faults, this thesis studies the effects of resistive unbalance and stator winding inter-turn short-circuits in PMSMs and compares two methods for detecting and discriminating both faults. These methods are based on monitoring and analyzing the third harmonic component of the stator currents and the first harmonic of the ZSVC. Finally, the Vold-Kalman filtering order tracking algorithm is introduced and applied to extract selected harmonics related to magnetic and electrical faults when the machine operates under variable speed and different load levels. Furthermore, different fault indicators are proposed and their behavior is validated by means of experimental data. Both simulation and experimental results show the potential of the proposed methods to provide helpful and reliable data to carry out a simultaneous diagnosis of resistive unbalance and stator winding inter-turn faults.

APA, Harvard, Vancouver, ISO, and other styles

9

Leith, Douglas. "Expert systems in A.C. induction motor fault diagnosis." Thesis, Robert Gordon University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280372.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Uliyar, Hithesh Sanjiva. "FAULT DIAGNOSIS OF VEHICULAR ELECTRIC POWER GENERATION AND STORAGE." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1284602099.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Electric motor fault diagnosis"

1

Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. Induction Motor Fault Diagnosis. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Loveday, George. Electronic fault diagnosis. 2nd ed. Harlow: Longman Scientific & Technical, 1986.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Fault diagnosis of digital circuits. Chichester: Wiley, 1990.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

Institution of Mechanical Engineers (Great Britain). Automobile Division., Institution of Mechanical Engineers (Great Britain). Railway Division., Institution of Mechanical Engineers (Great Britain). Power Industries Division., and International Federation of Automobile Engineers' and Technicians' Associations., eds. International Conference on Vehicle Condition Monitoring and Fault Diagnosis. London: Published for the Institution of Mechanical Engineers by Mechanical Engineering Publications, 1985.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Electrical machines diagnosis. London: ISTE, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Vehicle electronic systems and fault diagnosis: A practical guide for vehicle technicians. Warrendale, PA: STS Press, 1998.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Bonnick, Allan W. M. Vehicle electronic systems and fault diagnosis: A practical guide for vehicle technicians. London: Arnold, 1998.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection. Singapore: World Scientific, 1997.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Ding, Steven X. Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools. 2nd ed. London: Springer London, 2013.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

W, Sheppard John. Research perspectives and case studies in system test and diagnosis. Boston: Kluwer Aacdemic Publishers, 1998.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Electric motor fault diagnosis"

1

Gundewar, Swapnil K., and Prasad V. Kane. "Condition Monitoring and Fault Diagnosis of Induction Motor in Electric Vehicle." In Lecture Notes in Mechanical Engineering, 531–37. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0550-5_53.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

López-Cárdenas, Rodrigo, Luis Pastor Sánchez-Fernández, and Sergio Suárez-Guerra. "Computational Model for Electric Fault Diagnosis in Induction Motors." In Advances in Intelligent and Soft Computing, 453–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03156-4_46.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Silveira, Alexandre, Rui Esteves Araújo, and Ricardo de Castro. "Survey on Fault-Tolerant Diagnosis and Control Systems Applied to Multi-motor Electric Vehicles." In Technological Innovation for Sustainability, 359–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19170-1_39.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Lu, Lixin, Hui Li, Guiqin Li, and Peter Mitrouchev. "Fault Diagnosis of Massage Chair Motor Based on Wavelet Packet Algorithm." In Lecture Notes in Electrical Engineering, 153–59. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6318-2_19.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Yongying, Jiang. "Research of Condition Monitoring and Fault Diagnosis System for Induction Motor." In Electrical, Information Engineering and Mechatronics 2011, 1143–49. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2467-2_135.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Sharma, Vivek Kumar, Adil Usman, and Bharat Singh Rajpurohit. "Demagnetization Fault Diagnosis in BLDC Motor Using Low-Cost Hall Effect Sensors." In Lecture Notes in Electrical Engineering, 297–306. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4775-1_32.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Aravanis, Theofanis I., Tryfon-Chrysovalantis I. Aravanis, and Polydoros N. Papadopoulos. "Fault Diagnosis in Direct Current Electric Motors via an Artificial Neural Network." In Engineering Applications of Neural Networks, 488–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20257-6_42.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Sun, Qing, Guangping Liu, Demin Qiu, Qi Zhang, and Jiaqing Xi. "The Research on Servo DC Motor Fault Diagnosis with LVQ Neural Network Theory." In Lecture Notes in Electrical Engineering, 565–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44687-4_50.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Zheng, Xinchao, Zhengyu Xue, and Chidong Qiu. "Fault Diagnosis of Induction Motor Bearing Based on Multitaper Spectrum and Support Vector Machine." In Lecture Notes in Electrical Engineering, 199–207. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2862-0_20.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Ma, Yue, Lu Lin, and Fei Qi. "Fault Diagnosis of the Motor of Electro-Mechanical Transmission of the High Speed Rotorcraft." In Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019), 359–71. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0474-7_34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Electric motor fault diagnosis"

1

Wang, Rongrong, and Junmin Wang. "In-Wheel Motor Fault Diagnosis for Electric Ground Vehicles." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4050.

Full text

Abstract:

This paper presents an in-wheel motor fault diagnosis method for fault-tolerant control of four-wheel independently driven (4WID) electric vehicles. 4WID electric vehicle is one of the promising architectures for electric ground vehicles. While such a vehicle architecture greatly increases the flexibility for vehicle control, it also raises the requirements on system reliability, safety, and fault tolerance due to the increased number of actuators. A fault diagnosis approach for finding the faulty in-wheel motor/motor driver pair is developed. The proposed diagnosis approach does not need a precise knowledge on tire-road friction coefficient (TRFC). Robustness analysis shows that the approach can work well in the presence of tire modeling errors. Simulations using a high-fidelity, CarSim, full-vehicle model indicated the effectiveness of the proposed in-wheel motor/motor driver fault diagnosis approach.

APA, Harvard, Vancouver, ISO, and other styles

2

Zholbaryssov, Madi, and Azeem Sarwar. "Stator Diagnosis in Permanent Magnet Synchronous Motor (PMSM)." In ASME 2019 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ipack2019-6423.

Full text

Abstract:

Abstract GM has a vision of future with zero crashes, zero emissions, and zero congestion. Permanent Magnet Synchronous Motors will be integral part of an all-electric future, due to their excellent power to mass ratio and smaller size, which promises to deliver the zero emission world. Making sure that these motors do not fail abruptly without warning, will also reduce congestion caused on the roads by such incidents. Stator winding health monitoring presented in this article allows to detect a fault at its early stage, which greatly increases the chances of the customer being able to repair electric drive system before it completely fails. We present approach for detecting shorted turn faults in stator winding of permanent magnet synchronous motor. The approach is based on monitoring negative sequence admittance for certain operating conditions. Timely fault detection also allows to take preventive action to limit damage propagation across the electric drive, thus, reducing repair and warranty costs. The research presented in this article also furthers GM’s strategic initiative to develop Vehicle Health Management (VHM) technologies that positively impact customer ownership experiences and drive their long-term loyalty to GM brands.

APA, Harvard, Vancouver, ISO, and other styles

3

Li, Tianpei, Qadeer Ahmed, Giorgio Rizzoni, Jason Meyer, Mathew Boesch, and Bader Badreddine. "Motor Resolver Fault Propagation Analysis for Electrified Powertrain." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5408.

Full text

Abstract:

As an integral part of electrified powertrain, resolver is broadly used to do position and speed sensing for electric motors, subject to different types of resolver faults. This paper investigates the resolver fault propagation in electrified powertrain, with focus on the amplitude imbalance, quadrature imperfection and reference phase shift in the resolver position sensing system. The resolver fault effects in the Permanent Magnet Synchronous Machine (PMSM) drive system are first analyzed based on the mathematical model of a surface mounted PMSM with direct Field-oriented Control (FOC). Then the resolver fault propagation in the powertrain is studied in terms of two different motor operating conditions, motor torque control and motor speed control. Simulation is done in Matlab/Simulink based on the PMSM drive model and the powertrain-level simulator to verify the fault propagation analyses. The results can be used to help design the resolver fault diagnostic strategy and determine speed matching condition between engine and electric motor for mode transition control in hybrid electric vehicles.

APA, Harvard, Vancouver, ISO, and other styles

4

Zhang, Jiyu, Giorgio Rizzoni, and Qadeer Ahmed. "Fault Modelling for Hierarchical Fault Diagnosis and Prognosis." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3825.

Full text

Abstract:

Fault modeling, which is the determination of the effects of a fault on a system, is an effective way for conducting failure analysis and fault diagnosis for complex system. One of the major challenges of fault modeling in complex systems is the ability to model the effects of component-level faults on the system. This paper develops a simulation-based methodology for failure analysis through modeling component-level fault effect on the system level, with application to electric vehicle powertrains. To investigate how a component fault such as short circuit in a power switch or open circuit in a motor winding affects the vehicle system, this paper develops a detailed simulator which allows us to see system and subsystem failure behaviors by incorporating fault models in the system. This fault modeling process provides useful knowledge for designing a reliable and robust fault diagnosis and prognosis procedures for electrified powertrains.

APA, Harvard, Vancouver, ISO, and other styles

5

Harihara, Parasuram P., and Alexander G. Parlos. "Sensorless Detection and Isolation of Faults in Motor-Pump Systems." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66446.

Full text

Abstract:

Induction motors are the workhorses of industry and a lot of effort has been invested in detecting and diagnosing induction motor faults through the analysis of the motor electrical signals. However, in many industrial applications, electric motors are used to drive dynamic loads such as pumps, fans, blowers etc. Failure of either the motors or the driven loads is associated with operational disruption. Consequently it would be beneficial if the entire motor-pump system is monitored and diagnosed. The large costs associated with production losses can be avoided if system degradation can be detected at early stages prior to failure. Moreover, downtime can be further reduced if the faulty component within the drive power system can be isolated thereby aiding plant personnel to be better prepared with spares and repair kits. Hence there is not only a strong need for cost-effective detection schemes to assess the condition of the drive power system as a whole, but also a strong need for efficient isolation schemes to identify the component within the system that is faulty. This paper describes a sensorless approach to detect and isolate induction motor and/or centrifugal pump faults. Motor and pump bearing degradation is considered to validate the performance effectiveness of the proposed scheme. No add-on sensors, on either the motor or the pump, are used in the development of the proposed method to avoid any reduction in overall system reliability and prevent increased costs. In fact, motor and/or pump bearing degradation is detected and isolated using only the motor line voltages and phase currents. The proposed technique is insensitive to electric power supply fluctuations and mechanical load variations and it does not require prior knowledge of either the motor or the pump design parameters. Hence this approach can be easily ported to motor-pump systems of varying manufacturers and sizes. The developed algorithm has been tested on accelerated fault data collected from a centrifugal pump fluid loop driven by a 3-φ, 3 hp induction motor. Results from these experiments indicate that the proposed fault detection and isolation scheme successfully detects and classifies bearing degradation in the motor and/or the pump without false positives or misclassification.

APA, Harvard, Vancouver, ISO, and other styles

6

Gou, Xiaodong, Chong Bian, Fuping Zeng, Qingyang Xu, Wencai Wang, and Shunkun Yang. "A Data-Driven Smart Fault Diagnosis Method for Electric Motor." In 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2018. http://dx.doi.org/10.1109/qrs-c.2018.00053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Yuan, Jingxin, Yulei Wang, Shuyou Yu, and Hong Chen. "Gain-scheduled fault diagnosis of in-wheel motor electric vehicles." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7979217.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Cruz, S. M. A., and A. J. M. Cardoso. "Fault Indicators for the Diagnosis of Rotor Faults in FOC Induction Motor Drives." In 2007 IEEE International Electric Machines & Drives Conference. IEEE, 2007. http://dx.doi.org/10.1109/iemdc.2007.383590.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Mukhopadhyay, Rajarshi, Parth Sarathi Panigrahy, Gaurab Misra, and Paramita Chattopadhyay. "Quasi 1D CNN-based Fault Diagnosis of Induction Motor Drives." In 2018 5th International Conference on Electric Power and Energy Conversion Systems (EPECS). IEEE, 2018. http://dx.doi.org/10.1109/epecs.2018.8443552.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Harihara, Parasuram P., and Alexander G. Parlos. "Sensorless Detection of Impeller Cracks in Motor Driven Centrifugal Pumps." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66273.

Full text

Abstract:

Electrical signal analysis has been in use for quite some time to detect and diagnose induction motor faults. In most industrial applications, induction motors are used to drive dynamic loads such as pumps, compressors, fans, etc. Failure of either the motors or the driven loads results in an unscheduled downtime which in turn leads to loss of production. These operational disruptions could be avoided if the equipment degradation is detected in its early stages prior to reaching catastrophic failure conditions. Hence the need arises for cost-effective detection schemes not only for assessing the condition of electric motors but also the driven loads. This paper presents an experimentally demonstrated sensor-less approach to detect impeller cracks in centrifugal pumps. The proposed method is sensorless in the sense that it does not use any mechanical and/or process pump sensors to detect impeller faults. Rather motor electrical measurements are used for the intended purpose. Mechanical sensors have high costs and low reliability, and frequently fail more often than the system being monitored. Hence add-on mechanical sensors reduce the overall system reliability. In this study, fault detection is accomplished using only the line voltages and phase currents of the electric motor driving the pump. The developed detection algorithm is insensitive to electrical power supply and load variations. Furthermore, it does not require prior knowledge of either a motor or the pump model or design parameters and hence the detection algorithm can be easily ported to motor-pump systems of varying manufacturers and sizes. The proposed fault detection scheme has been tested on data collected from a centrifugal pump driven by a 3-φ, 3 hp induction motor. Several cracks on the pump impeller are staged to validate the detection effectiveness of the proposed scheme and compare its effectiveness with respect to continuous vibration monitoring. In addition to these staged faults, experiments are also conducted to demonstrate the prevention of false alarms by the algorithm. Results from all of these experiments are presented to substantiate the performance of the sensorless pump fault detection scheme.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!