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Journal articles on the topic 'Motor fault diagnosis'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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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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2

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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3

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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4

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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5

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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8

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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9

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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10

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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11

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.

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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

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12

Gu, Yufeng, Yongji Zhang, Mingrui Yang, and Chengshan Li. "Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information." Applied Sciences 13, no. 7 (2023): 4192. http://dx.doi.org/10.3390/app13074192.

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The motor is the primary impetus source of most mechanical equipment, and its failure will cause substantial economic losses and safety problems. Therefore, it is necessary to study online fault diagnosis techniques for motors, given the problems caused by shallow learning models or single-sensor fault analysis in previous motor fault diagnosis techniques, such as blurred fault features, inaccurate identification, and time and manpower consumption. In this paper, we proposed a model for motor fault diagnosis based on deep learning and multi-sensor information fusion. Firstly, a correlation ada

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Zhang, Yahui, and Kai Yang. "Fault Diagnosis of Submersible Motor on Offshore Platform Based on Multi-Signal Fusion." Energies 15, no. 3 (2022): 756. http://dx.doi.org/10.3390/en15030756.

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As an important production equipment of the offshore platform, the operation reliability of submersible motors is critical to oil and gas production, natural gas energy supplies, and social and economic benefits, etc. In order to realize the health management and fault diagnosis of submersible motors, a motor fault-monitoring method based on multi-signal fusion is proposed. The current signals and vibration signals were selected as characteristic signals. Through fusion correlation analysis, the correlation between different signals was established to enhance the amplitude at the same frequenc

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14

Rahmatullah, Rohullah, Necibe Fusun Oyman Serteller, and Vedat Topuz. "Modeling and Simulation of Faulty Induction Motor in DQ Reference Frame Using MATLAB/SIMULINK with MATLAB/GUIDE for Educational Purpose." International Journal of Education and Information Technologies 17 (March 13, 2023): 7–20. http://dx.doi.org/10.46300/9109.2023.17.2.

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Owing to their robust structure, induction motors are preferred to be used under difficult working conditions. Therefore, various faults may occur in the motor due to unexpected forces during the operations. Obtaining the data through experimental methods by physically creating faults in the induction motors, and analyzing their behavior is not efficient in terms of cost and time for educational purposes. Considering the above negative situation, in this paper, mathematical models have been developed in the dq0 stationary reference frame expressing three-phase stator windings short-circuit fau

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15

Sabna M. "A Single Detection And Diagnosis Algorithm For Electrical Faults in a Five-Phase.Permanent.Magnet Synchronous Motor Drive." Journal of Electrical Systems 20, no. 11s (2024): 3369–87. https://doi.org/10.52783/jes.8091.

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In all processing and manufacturing industries, approximately half of the operating cost is contributed to the maintenance process. Due to high reliability, and fault-tolerant capability, five-phase Permanent Magnet Synchronous Motors (5ϕ-PMSM) are commonly used in high-power and fault-tolerant applications. Early-stage detection and diagnosis of faults can reduce maintenance costs. This paper proposes a single algorithm for detecting and diagnosing electrical faults such as inter-turn short circuit faults, phase-to-phase faults, phase-to-ground to ground faults, and open circuit faults in a 5

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16

Liu, Chencheng, and Aiming Zhao. "Asynchronous motor fault diagnosis method based on BiLSTM network." Journal of Physics: Conference Series 2902, no. 1 (2024): 012019. https://doi.org/10.1088/1742-6596/2902/1/012019.

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Abstract Asynchronous motor stator short-circuit faults can pose a great threat to the production and operation of asynchronous motors. To identify the short-circuit faults and recover the losses in time, this paper establishes a model of a three-phase asynchronous motor in Simulink by using MATLAB, and simulations are carried out under various fault conditions. The time and frequency domain characterization of signals are extracted by fast Fourier transforms (FFT) and input into the bidirectional long short-term memory (BiLSTM), for training and learning. The experiments show that the overall

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17

Sun, Tianang, Pak-Kin Wong, and Xiaozheng Wang. "Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control." Vehicles 6, no. 1 (2023): 93–119. http://dx.doi.org/10.3390/vehicles6010004.

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Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the dif

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18

Liu, Xiaopeng, Jianfeng Hong, Kang Zhao, Bingxiang Sun, Weige Zhang, and Jiuchun Jiang. "Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks." Machines 11, no. 12 (2023): 1061. http://dx.doi.org/10.3390/machines11121061.

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Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromag

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19

Zhang, Xi, and Hui Lin. "Fault Diagnosis and Compensation Strategy of BLDC Motor Drives with Hall Sensors." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 6 (2019): 1278–84. http://dx.doi.org/10.1051/jnwpu/20193761278.

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In a brushless DC motor drive system, two fault diagnosis methods were proposed in order to investigate the faults of Hall position sensors, and the corresponding compensation strategy was carried out. Firstly, the differences of Hall signal sequences during normal and fault situation of motor were analyzed, then a fault diagnosis method based on the characteristics of Hall signal sequences was proposed. In order to detect the faults of Hall position sensors in real time, a sliding mode-based super-twisting speed observer was established and combined with the Hall signal sequences. Under fault

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20

Zhang, Meng, and Yinquan Yu. "Permanent magnet synchronous motor demagnetization fault diagnosis based on leakage radial magnetic density." Journal of Physics: Conference Series 2708, no. 1 (2024): 012008. http://dx.doi.org/10.1088/1742-6596/2708/1/012008.

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Abstract A method of demagnetization fault diagnosis of permanent magnet synchronous motors is proposed by measuring the radial magnetic density of the leakage field at the external space point of the motor. The method enables non-invasive diagnosis of motor permanent magnet demagnetization faults compared to other methods. In this paper, an analytical model of the radial magnetic density of the leakage field of a surface-mounted permanent magnet synchronous motor under no-load conditions is proposed, and its accuracy is verified by simulation. The influence mode of demagnetization fault on th

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21

Yuan, Wei, Julong Chen, and Xingji Yu. "Research on Fault Diagnosis of Ship Propulsion System Based on Improved Residual Network." Journal of Marine Science and Engineering 13, no. 1 (2025): 70. https://doi.org/10.3390/jmse13010070.

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In ship propulsion, accurately diagnosing faults in permanent magnet synchronous motor is essential but challenging due to limitations in the intuitive characterization and feature extraction of fault signals. This study presents an innovative approach to motor fault detection by integrating phase-contrastive current dot patterns with an enhanced residual network, enhancing the diagnostic effect. Initially, the research involves creating a dataset that simulates stator currents. It is achieved through mathematical modeling of two common faults in permanent magnet synchronous motors: inter-turn

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22

Gmati, Badii, Amine Ben Rhouma, Houda Meddeb, and Sejir Khojet El Khil. "Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm." World Electric Vehicle Journal 15, no. 2 (2024): 53. http://dx.doi.org/10.3390/wevj15020053.

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Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transforma

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23

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.

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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 quadrati

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Qian, Long, Binbin Li, and Lijuan Chen. "CNN-Based Feature Fusion Motor Fault Diagnosis." Electronics 11, no. 17 (2022): 2746. http://dx.doi.org/10.3390/electronics11172746.

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Artificial intelligence fields have been using deep learning in recent years. Due to its powerful data mining capabilities, deep learning has a wide-ranging impact on the diagnosis of motor faults. A method for diagnosing motor faults based on the multi-feature fusion of convolutional neural network (CNN) is presented in this paper. As far as the method is concerned, CNN is used as the basic framework, and the CNN model has been improved. First, the collected vibration and current signals are preprocessed. Second, segmented multi-time window synchronous input is performed on the processed data

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Hasan, Shakir Majdi, Sadey Shijer Sameera, Owaid Hanfesh Abduljabbar, Jaafer Habeeb Laith, and H. Sabry Ahmad. "Analysis of fault diagnosis of DC motors by power consumption pattern recognition." Eastern-European Journal of Enterprise Technologies 5, no. 5 (113) (2021): 14–20. https://doi.org/10.15587/1729-4061.2021.240262.

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Early detection of faults in DC motors extends their life and lowers their power usage. There are a variety of traditional and soft computing techniques for detecting faults in DC motors. Many diagnostic techniques have been developed in the past to detect such fault-related patterns. These methods for detecting the aforementioned potential failures of motors can be utilized in a variety of scientific and technological domains. Motor Power Pattern Analysis (MPPA) is a technology that analyzes the current and voltage provided to an electric motor using particular patterns and protocols to asses

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26

Jiang, Yutao, Baojian Ji, Jin Zhang, Jianhu Yan, and Wenlong Li. "An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles." World Electric Vehicle Journal 15, no. 4 (2024): 165. http://dx.doi.org/10.3390/wevj15040165.

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This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techni

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27

Yang, Su Fei, and Xiao Pin Wu. "The Application of Multi-Agent Technology in the Fault Diagnosis System of Motor Armature." Applied Mechanics and Materials 84-85 (August 2011): 106–9. http://dx.doi.org/10.4028/www.scientific.net/amm.84-85.106.

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Insufficient welding or bad welding of motor armature is one of the main faults of motor armature. With the help of the application of multi-agent technology in the fault diagnosis system of motor armature to detect insufficient welding or bad welding, the intelligent description of the various faults has been enhanced, also, the capacity of fault diagnosis of the system have been greatly optimized and improved. In this paper, we first analyses the characteristics of the fault of motor armature with insufficient welding or bad welding, by which the characteristics of the fault are identified.

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Li, Qing-Yuan, Pak-Kin Wong, Chi-Man Vong, Kai Fei, and In-Neng Chan. "A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning." Electronics 13, no. 1 (2023): 108. http://dx.doi.org/10.3390/electronics13010108.

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Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting a

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Ye, Ming, Run Gong, Wanjun Wu, Zhiyuan Peng, and Kelin Jia. "Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Wavelet Packet Transform and Genetic Algorithm-Optimized Back Propagation Neural Network." World Electric Vehicle Journal 16, no. 4 (2025): 238. https://doi.org/10.3390/wevj16040238.

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In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase short-circuit, loss of magnetism, inverter open-circuit, rotor eccentricity), a corresponding motor fault model is established. The stator current signals during motor operation are analyzed using wavelet packet transform, and energy features are extracted from them as fea

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A., A. Alawady, F. M. Yousof M., Azis N., and A. Talib M. "Frequency response analysis technique for induction motor short circuit faults detection." International Journal of Power Electronics and Drive System (IJPEDS) 11, no. 3 (2020): 1653–59. https://doi.org/10.11591/ijpeds.v11.i3.pp1653-1659.

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The paper presents the description for diagnostic methods of induction motor's stator windings fault. The presented methods use Frequency Response Analysis (FRA) technique for detection of Winding Faults in Induction Motor . This method is previously reliable method for faults diagnosis and detection in many parts of transformers including transformer windings. In this paper, this method was used for motor windings faults detection. This paper presents the FRA response interpretation on internal short circuit (SC) fault at stator winding on three cases studies of different three-phase indu

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Chen, Yong, Siyuan Liang, Wanfu Li, Hong Liang, and Chengdong Wang. "Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review." Applied Sciences 9, no. 10 (2019): 2116. http://dx.doi.org/10.3390/app9102116.

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Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intellig

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Adamou, Adamou Amadou, and Chakib Alaoui. "Efficiency-Centered Fault Diagnosis of In-Service Induction Motors for Digital Twin Applications: A Case Study on Broken Rotor Bars." Machines 12, no. 9 (2024): 604. http://dx.doi.org/10.3390/machines12090604.

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The uninterrupted operation of induction motors is crucial for industries, ensuring reliability and continuous functionality. To achieve this, we propose an innovative approach that utilizes an efficiency model-based digital shadow system for in situ failure detection and diagnosis (FDD) in induction motors (IMs). The shadow model accurately estimates IM losses and efficiency across various operational conditions. Our proposed method utilizes efficiency as the primary indicator for fault detection, while losses serve as condition indicators for fault diagnosis based on real-time motor paramete

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Majdi, Hasan Shakir, Sameera Sadey Shijer, Abduljabbar Owaid Hanfesh, Laith Jaafer Habeeb, and Ahmad H. Sabry. "Analysis of fault diagnosis of DC motors by power consumption pattern recognition." Eastern-European Journal of Enterprise Technologies 5, no. 5 (113) (2021): 14–20. http://dx.doi.org/10.15587/1729-4061.2021.240262.

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Abstract:

Early detection of faults in DC motors extends their life and lowers their power usage. There are a variety of traditional and soft computing techniques for detecting faults in DC motors. Many diagnostic techniques have been developed in the past to detect such fault-related patterns. These methods for detecting the aforementioned potential failures of motors can be utilized in a variety of scientific and technological domains. Motor Power Pattern Analysis (MPPA) is a technology that analyzes the current and voltage provided to an electric motor using particular patterns and protocols to asses

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Im, Seong-Hwan, and Bon-Gwan Gu. "Study of Induction Motor Inter-Turn Fault Part I: Development of Fault Models with Distorted Flux Representation." Energies 15, no. 3 (2022): 894. http://dx.doi.org/10.3390/en15030894.

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An inter-turn fault (ITF) is one of the most frequent induction motor faults; thus, many previous works have studied its model and diagnosis. However, previous works, simplifying the specific distorted flux distribution by the ITF, presented induction motor fault models and focused on the fault signal analysis for diagnoses. Consequently, these results are only adequate for the pretested motor and sensitive to fault signal distortion. This paper presents an induction motor ITF model in the stationary DQ frame, for a model-based diagnosis. Furthermore, to describe the distorted flux distributio

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Pan, Shuang, Tian Han, Andy C. C. Tan, and Tian Ran Lin. "Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm." Shock and Vibration 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5836717.

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An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction

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Wang, Xueqing, Zheng Wang, Wei Wang, and Ming Cheng. "Fault Diagnosis of Sensors for T-type Three-Level Inverter-fed Dual Three-Phase Permanent Magnet Synchronous Motor Drives." Power Electronics and Drives 4, no. 1 (2019): 167–78. http://dx.doi.org/10.2478/pead-2018-0012.

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AbstractTo improve the reliability of motor system, this paper investigates the sensor fault diagnosis methods for T-type inverter-fed dual three-phase permanent magnet synchronous motor (PMSM) drives. Generally, a T-type three-level inverter-fed dual three-phase motor drive utilizes four phase-current sensors, two direct current (DC)-link voltage sensors and one speed sensor. A series of diagnostic methods have been comprehensively proposed for the three types of sensor faults. Both the sudden error change and gradual error change of sensor faults are considered. Firstly, the diagnosis of spe

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Shi, Cenwei, Lin Peng, Zhen Zhang, and Tingna Shi. "Analytical Modeling and Analysis of Permanent-Magnet Motor with Demagnetization Fault." Sensors 22, no. 23 (2022): 9440. http://dx.doi.org/10.3390/s22239440.

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Factors such as insufficient heat dissipation and excessively high temperature can easily lead to demagnetization of the PMs in permanent-magnet (PM) motors. As a result, the magnetic field distribution of the motor will not be uniform, producing fault harmonics and lowering the operational performance of the motor. An essential stage in the diagnosis of faults and the monitoring of motor condition is the establishment of an accurate model of motors with demagnetization faults. In this paper, demagnetization faults are modeled by changing the Fourier coefficients in the Fourier expansion of th

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Yin, Xiu, Xiyu Liu, Minghe Sun, Jianping Dong, and Gexiang Zhang. "Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis." Entropy 24, no. 10 (2022): 1385. http://dx.doi.org/10.3390/e24101385.

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The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characteriz

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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 (2021): 311–19. http://dx.doi.org/10.1108/sr-02-2021-0052.

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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 patte

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Du, Qiang, Danjiang Zhu, and Ming Ni. "Fault diagnosis of brushless DC motor based on Stack Sparse Autoencoder." Journal of Physics: Conference Series 2674, no. 1 (2023): 012033. http://dx.doi.org/10.1088/1742-6596/2674/1/012033.

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Abstract Because of their simple structure, long service life, high efficiency, etc., brushless DC (BLDC) motors have been widely applied in many fields. In some applications, high requirements for BLDC continuous use of motors, so often to BLDC running state monitoring of motors, realizes the early fault diagnosis, to improve the reliability, safety, and prolonged use. To solve this problem, a BLDC fault diagnosis method based on Fast Fourier Transform (FFT), Stacked Sparse Auto-encoder (SSAE), and soft classifier was proposed. In the laboratory, a BLDC model R-3525 was used as the experiment

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Kiter, Riyah Najim, Mohammed Moanes Ezzaldean, Yousif Ismail Almashhdany, and Fuad Lateef Salim. "Detection and Diagnosis of Induction Motor Faults by Intelligent Techniques." Journal of Engineering 23, no. 1 (2017): 29–47. http://dx.doi.org/10.31026/j.eng.2017.01.03.

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This paper presents a complete design and implementation of a monitoring system for the operation of the three-phase induction motors. This system is built using a personal computer and two types of sensors (current, vibration) to detect some of the mechanical faults that may occur in the motor. The study and examination of several types of faults including (ball bearing and shaft misalignment faults) have been done through the extraction of fault data by using fast Fourier transform (FFT) technique. Results showed that the motor current signature analysis (MCSA) technique, and measurement of

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Hoang Van Tung, Nguyen Van Khanh, and Nguyen Chi Ngon. "Proposal of noninvasive failure diagnosis of electrical motor using googlenet." Journal of Technical Education Science, no. 66 (October 28, 2021): 83–93. http://dx.doi.org/10.54644/jte.66.2021.1070.

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Fault diagnosis is a useful tool that reduces system maintenance risks and costs. However, data related to the system's nominal and fault operating behavior is often not collected and stored adequately, it is difficult to identify and suggest automated fault detection methods. This study proposes a solution to apply deep learning technique on the convolutional neural network (CNN) to identify some common errors on induction motors based on operation sound. The opreration sound signal emitted from on a 0.37 kW two-pole induction motor is collected in some cases such as normal operation, phase l

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Lopes, Tiago Drummond, Adroaldo Raizer, and Wilson Valente Júnior. "The Use of Digital Twins in Finite Element for the Study of Induction Motors Faults." Sensors 21, no. 23 (2021): 7833. http://dx.doi.org/10.3390/s21237833.

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Induction motors play a key role in the industrial sector. Thus, the correct diagnosis and classification of faults on these machines are important, even in the initial stages of evolution. Such analysis allows for increased productivity, avoids unexpected process interruptions, and prevents damage to machines. Usually, fault diagnosis is carried out by analyzing the characteristic effects caused by the faults. Thus, it is necessary to know and understand the behavior during the operation of the faulty machine. In general, monitoring these characteristics is complex, as it is necessary to acqu

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Wu, Hao, Xue Ma, and Chenglin Wen. "Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform." Sensors 22, no. 4 (2022): 1310. http://dx.doi.org/10.3390/s22041310.

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Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and its motor control system toward a low-carbon future, reduce carbon emissions, and improve the industrial economy and social economic efficiency. Due to high-speed, long-period, and heavy-load operation, various faults occur; since the existing integer-order Fourier transform methods have not enough able to detect fractional-order faults and lack robustness, it is difficult to realize the fine diagnosis of motor faults, which reduces the

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Mu-Zhuo Zhang, Mu-Zhuo Zhang, and Peng-Jie Du Mu-Zhuo Zhang. "Research on Three Common Fault Diagnosis Methods for AC Asynchronous Motors Based on Deep Learning." 電腦學刊 34, no. 6 (2023): 153–62. http://dx.doi.org/10.53106/199115992023123406012.

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<p>In response to the problem that traditional fault diagnosis methods mainly rely on manual search, this paper proposes an improved convolutional neural network based three item asynchronous motor fault diagnosis method. Taking the motor rotor bar fault as the research object, in the early stage of the fault, the characteristic signal is easily mixed with the motor fundamental frequency signal. Therefore, first, the current characteristics of the motor rotor bar fault are analyzed, and then the motor vibration signal is converted into a time-frequency map using wavelet analysis method.

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Gong, Xiaoyun, Zeheng Zhi, Kunpeng Feng, Wenliao Du, and Tao Wang. "Improved DCNN Based on Multi-Source Signals for Motor Compound Fault Diagnosis." Machines 10, no. 4 (2022): 277. http://dx.doi.org/10.3390/machines10040277.

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Induction motors, the key equipment for rotating machinery, are prone to compound faults, such as a broken rotor bars and bearing defects. It is difficult to extract fault features and identify faults from a single signal because multiple fault features overlap and interfere with each other in a compound fault. Since current signals and vibration signals have different sensitivities to broken rotor and bearing faults, a multi-channel deep convolutional neural network (MC-DCNN) fault diagnosis model based on multi-source signals is proposed in this paper, which integrates the original signals o

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Seera, Manjeevan, Chee Peng Lim, and Dahaman Ishak. "Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network." International Journal of Natural Computing Research 3, no. 1 (2012): 44–55. http://dx.doi.org/10.4018/jncr.2012010104.

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In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distingui

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Chang, Lien-Kai, Shun-Hong Wang, and Mi-Ching Tsai. "Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering." Energies 13, no. 17 (2020): 4467. http://dx.doi.org/10.3390/en13174467.

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In recent years, many motor fault diagnosis methods have been proposed by analyzing vibration, sound, electrical signals, etc. To detect motor fault without additional sensors, in this study, we developed a fault diagnosis methodology using the signals from a motor servo driver. Based on the servo driver signals, the demagnetization fault diagnosis of permanent magnet synchronous motors (PMSMs) was implemented using an autoencoder and K-means algorithm. In this study, the PMSM demagnetization fault diagnosis was performed in three states: normal, mild demagnetization fault, and severe demagnet

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He, Xiao, Yamei Ju, Yang Liu, and Bangcheng Zhang. "Cloud-Based Fault Tolerant Control for a DC Motor System." Journal of Control Science and Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/5670849.

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The fault tolerant control problem for a DC motor system is investigated in a cloud environment. Packet dropout phenomenon introduced by the limited-capacity communication channel is considered. Actuator faults are taken into consideration and fault diagnosis and fault tolerant control methods towards actuator faults are proposed to enhance the reliability of the whole cloud-based DC motor system. The fault diagnosis unit is then established with purpose of obtaining fault information. When the actuator fault is detected by comparing the residual signal with a predefined threshold, a residual

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Shao, Ke Yong, Li Juan Han, Xin Min Wang, Feng Wu Zhang, and Kun Qian. "Study of the Fault Diagnosis Based on Wavelet and Neural Network for the Motor." Applied Mechanics and Materials 433-435 (October 2013): 483–88. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.483.

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In the motor fault diagnosis technology, vibration signals can fully reflect the motor operation conditions. In this paper, a linear motor fault diagnosis method based on wavelet packet and neural network was presented. The improved neural network system was designed with variable hidden layer neurons. The network chosen different numerical values depending on different situations to reach the requirements that improving diagnostic accuracy and shortening the diagnosis time. The linear motor’s normal and two common faults vibration signals were analyzed and the vibration signals energy charact

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