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Relevant bibliographies by topics / Motor fault diagnosis

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Academic literature on the topic 'Motor fault diagnosis'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Motor fault diagnosis"

1

Chu, Kenny Sau Kang, Kuew Wai Chew, Yoong Choon Chang, and Stella Morris. "An Open-Circuit Fault Diagnosis System Based on Neural Networks in the Inverter of Three-Phase Permanent Magnet Synchronous Motor (PMSM)." World Electric Vehicle Journal 15, no. 2 (2024): 71. http://dx.doi.org/10.3390/wevj15020071.

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Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit fault diagnosis system faces challenges in precisely detecting specific faulty switches. The proposed work presents a neural network-based open-circuit fault diagnosis system for identifying faulty power switches in inverter-driven motor systems. The system leverages trained phase-

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Lee, Jong-Hyun, Jae-Hyung Pack, and In-Soo Lee. "Fault Diagnosis of Induction Motor Using Convolutional Neural Network." Applied Sciences 9, no. 15 (2019): 2950. http://dx.doi.org/10.3390/app9152950.

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Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction mo

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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 (2003): 80–95. http://dx.doi.org/10.1115/1.1543550.

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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

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Glowacz, A., W. Glowacz, Z. Glowacz, et al. "Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers." Archives of Metallurgy and Materials 62, no. 4 (2017): 2413–19. http://dx.doi.org/10.1515/amm-2017-0355.

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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

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Hsueh, Yu-Min, Veeresh Ramesh Ittangihal, Wei-Bin Wu, Hong-Chan Chang, and Cheng-Chien Kuo. "Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform." Symmetry 11, no. 10 (2019): 1212. http://dx.doi.org/10.3390/sym11101212.

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Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two d

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Liu, Bohai, Qinmu Wu, Zhiyuan Li, and Xiangping Chen. "Research on Fault Diagnosis of IPMSM for Electric Vehicles Based on Multi-Level Feature Fusion SPP Network." Symmetry 13, no. 10 (2021): 1844. http://dx.doi.org/10.3390/sym13101844.

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At this stage, the fault diagnosis of the embedded permanent magnet synchronous motor (IPMSM) mostly relies on the analysis of related signals when the motor is running. It requires designers to deeply understand the motor drive system and fault characteristic signals, which leads to a high threshold for fault diagnosis. This study proposes an IPMSM fault diagnosis method based on a multi-level feature fusion spatial pyramid pooling (SPP) network, which can directly diagnose motor faults through motor operating current data. This method uses the finite element software Altair Flux to build sym

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Bergesen Husebø, Arild, Surya Teja Kandukuri, Andreas Klausen, Van Khang Huynh, and Kjell Gunnar Robbersmyr. "Rapid Diagnosis of Induction Motor Electrical Faults using Convolutional Autoencoder Feature Extraction." PHM Society European Conference 5, no. 1 (2020): 10. http://dx.doi.org/10.36001/phme.2020.v5i1.1247.

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Electrical faults such as stator turns fault and broken rotor bars are among the frequently occurring failure modes in induction motors. This article presents a novel deep learning-based approach for the rapid diagnosis of these electrical faults within a short time window of 200 milliseconds. The extended Park's vector, calculated using three-phase supply currents, is chosen as the medium for fault detection. An unsupervised convolutional autoencoder is designed to detect features distinguishing healthy and faulty conditions. The developed features are supplied to a support vector machine to

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Zhang, Xuhao, Kun Han, Hu Cao, Ziying Wang, and Ke Huo. "Fault Injection Model of Induction Motor for Stator Interturn Fault Diagnosis Research Based on HILS." World Electric Vehicle Journal 12, no. 4 (2021): 170. http://dx.doi.org/10.3390/wevj12040170.

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Recently, in order to ensure the reliability and safety of trains, online condition monitoring and fault diagnosis of traction induction motors have become active issues in the area of rail transportation. The fault diagnosis algorithm can be developed and debugged in a real-time environment based on hardware-in-the-loop simulation (HILS). However, the dynamic space model of induction motors with stator interturn short-circuit faults faces the problem that the faulty state and the healthy state are not compatible, which is inconvenient for the HILS. In this paper, a fault injection model is pr

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Lin, Shih-Lin. "Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults." Sensors 21, no. 18 (2021): 6065. http://dx.doi.org/10.3390/s21186065.

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Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fau

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Papathanasopoulos, Dimitrios A., Konstantinos N. Giannousakis, Evangelos S. Dermatas, and Epaminondas D. Mitronikas. "Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives." Energies 14, no. 8 (2021): 2248. http://dx.doi.org/10.3390/en14082248.

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A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed

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Dissertations / Theses on the topic "Motor fault diagnosis"

1

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.

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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.

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Alwodai, Ahmed. "Motor fault diagnosis using higher order statistical analysis of motor power supply parameters." Thesis, University of Huddersfield, 2015. http://eprints.hud.ac.uk/id/eprint/26620/.

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Motor current signature analysis (MCSA) has been an effective method to monitor electrical machines for many years, predominantly because of its low instrumentation cost, remote implementation and comprehensive information contents. However, it has shortages of low accuracy and efficiency in resolving weak signals from incipient faults, such as detecting early stages of induction motor fault. In this thesis MCSA has been improved to accurately detect electrical and mechanical faults in the induction motor namely broken rotor bars, stator faults and motor bearing faults. Motor current signals c

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Sangha, Mahavir Singh. "Intelligent fault diagnosis for automative engines and real data evaluation." Thesis, Liverpool John Moores University, 2008. http://researchonline.ljmu.ac.uk/5867/.

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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.

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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.

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Wanner, Daniel, Trigell Annika Stensson, Lars Drugge, and Jenny Jerrelind. "Survey on fault-tolerant vehicle design." KTH, Farkost och flyg, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98811.

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Fault-tolerant vehicle design is an emerging inter-disciplinary research domain, which is of increasedimportance due to the electrification of automotive systems. The goal of fault-tolerant systems is to handleoccuring faults under operational condition and enable the driver to get to a safe stop. This paperpresents results from an extended survey on fault-tolerant vehicle design. It aims to provide a holisticview on the fault-tolerant aspects of a vehicular system. An overview of fault-tolerant systems in generaland their design premises is given as well as the specific aspects related to aut

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Awadallah, Mohamed Abdel-Azim Mohamed. "Automatic fault diagnosis and location in CSI-fed brushless DC motor drives using Neuro-Fuzzy Systems /." Search for this dissertation online, 2004. http://wwwlib.umi.com/cr/ksu/main.

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Bradley, William J. "Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.

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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous

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Bradley, William John. "Current based fault detection and diagnosis of induction motors : adaptive mixed-residual approach for fault detection and diagnosis of rotor, stator, bearing and air-gap faults in induction motors using a fuzzy logic classifier with voltage and current measurement only." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/7265.

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Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous

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Books on the topic "Motor fault diagnosis"

1

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

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Saad, Nordin, Muhammad Irfan, and Rosdiazli Ibrahim. Condition Monitoring and Faults Diagnosis of Induction Motors. CRC Press, 2018. http://dx.doi.org/10.1201/9781351172561.

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3

Coombes, Peter. Fuel injection manual: Test procedures, service adjustments and fault diagnosis for petrol fuel injection systems. Haynes, 1990.

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4

Jendham, Inc. General Motors fuel injection systems: Diagnosis of code & no code faults with serial & PID data for 1987-1998 cars : quick reference guide. Jendham, 1998.

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Inc, Jendham, ed. General Motors fuel injection systems: Diagnosis of code and no code faults with serial & PID data for cars from 1993-2001 : quick reference guide. Jendham, 2000.

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6

Chattopadhyay, Surajit, Madhuchhanda Mitra, Samarjit Sengupta, and Subrata Karmakar. Induction Motor Fault Diagnosis: Approach through Current Signature Analysis. Springer, 2016.

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7

Chattopadhyay, Surajit, Madhuchhanda Mitra, Samarjit Sengupta, and Subrata Karmakar. Induction Motor Fault Diagnosis: Approach Through Current Signature Analysis. Springer London, Limited, 2016.

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Chattopadhyay, Surajit, Madhuchhanda Mitra, Samarjit Sengupta, and Subrata Karmakar. Induction Motor Fault Diagnosis: Approach through Current Signature Analysis. Springer, 2018.

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9

Lang, Wangjie. AI Techniques in EV Motor and Inverter Fault Detection and Diagnosis. Institution of Engineering & Technology, 2024.

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Hu, Yihua, Xiaotian Zhang, and Wangjie Lang. AI Techniques in EV Motor and Inverter Fault Detection and Diagnosis. Institution of Engineering and Technology, 2023. http://dx.doi.org/10.1049/pbtr043e.

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Book chapters on the topic "Motor fault diagnosis"

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Stator Winding Fault." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_6.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Induction Motor and Faults." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_2.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Introduction." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_1.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Analytical Tools for Motor Fault Diagnosis." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_3.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Broken Rotor Bar." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_4.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Rotor Mass Unbalance." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_5.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Single Phasing of an Induction Motor." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_7.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Crawling of an Induction Motor." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_8.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. "Induction Motor Fault Diagnosis: General Discussion and Research Scope." In Induction Motor Fault Diagnosis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1_9.

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Isermann, Rolf. "Fault detection and diagnosis of DC motor drives." In Fault-Diagnosis Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30368-5_20.

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Conference papers on the topic "Motor fault diagnosis"

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Zhang, Ying, Pingshu Ge, Tao Zhang, and Yang Wang. "Motor Bearing Fault Diagnosis based on Improved MobilenetV2." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696838.

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Ma, Yi, and Jun Li. "Motor Bearing Fault Diagnosis Based on GAM-CNN." In 2024 Boao New Power System International Forum - Power System and New Energy Technology Innovation Forum (NPSIF). IEEE, 2024. https://doi.org/10.1109/npsif64134.2024.10883406.

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Cui, Yibo, Yu Liao, Yuhang Hu, Kun Cui, and Ximeng Wang. "Motor Fault Diagnosis Based on BiLSTM and GNN." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065097.

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Ma, Runyu, and Bin Jiao. "Motor bearing fault diagnosis method based on GJO-LSSVM." In International Conference on Electronics. Electrical and Information Engineering, edited by Shengqing Li and Bin Hu. SPIE, 2024. https://doi.org/10.1117/12.3052164.

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Guo, Peikun, Dehong Liu, Bingnan Wang, Yebin Wang, Hiroshi Inoue, and Makoto Kanemaru. "Deep Generalized Canonical Correlation Analysis for Motor Fault Diagnosis." In 2025 IEEE Industry Applications Society Annual Meeting (IAS). IEEE, 2025. https://doi.org/10.1109/ias62731.2025.11061634.

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Merabet, Nacer, Fatima Babaa, Abderrahim Touil, and Oualid Abd Elghani Chibani. "Combined-Fault Detection and Diagnosis in Induction Motor Using Motor Current Signature Analysis." In 2024 3rd International Conference on Advanced Electrical Engineering (ICAEE). IEEE, 2024. https://doi.org/10.1109/icaee61760.2024.10783215.

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Mechefske, Chris K., and Lingxin Li. "Induction Motor Fault Detection and Diagnosis Using Artifical Neural Networks." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84215.

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This paper investigates induction motor fault detection and diagnosis using Artificial Neural Networks (ANN). The ANN techniques include feedforward backpropagation networks (FFBPN) and self organizing maps (SOM), used individually and in combination. Common induction motor faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using dynamic measurements of stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs ar

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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.

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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 pr

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Wang, Chunjian, Beshah Ayalew, and Zoran Filipi. "Fault Diagnosis on a Digital-Displacement Pump/Motor." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3967.

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A Digital-Displacement Pump/Motor (DDPM) has recently been proposed as an attractive candidate for hydraulic powertrain applications. A DDPM uses solenoid-controlled valves for each cylinder. This provision offers flexibility of control that can be exploited to boost system efficiency by matching individual cylinder operations with load conditions. However, the added complexity from individual cylinder control necessitates mechanisms for fault diagnosis and control reconfiguration to ensure reliable operation of the DDPM. Furthermore, available measurements are often limited to supply and retu

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Moghadam, Armin, and Fatemeh Davoudi Kakhki. "Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003049.

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Bearing failure highly impacts performance and production of manufacturing systems, causes safety incidents, and results in casualties and property loss. According to the current literature, bearing faults cause 30-40% of all failures in induction motors. Therefore, identification of bearing faults, at early stages, is crucial to ensure seamless and reliable operation of induction motors in industrial and manufacturing operations. Faults occur in four components of bearing: inner race, outer race, ball, and cage. Regardless of the component in which fault occurs, it causes changes in vibration

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