Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Abstract

Induction motors (IMs) are the workhorse of the industry and are subjected to a harsh environment. Due to their operating conditions, they are exposed to different kinds of unavoidable faults that lead to unscheduled downtimes and losses. These faults may be detected early through predictive maintenance (i.e., deployment of condition monitoring systems). Motor current signature analysis (MCSA) is the most widely used technique to detect various faults in industrial motors. The stator winding faults (SWF) are one of the major faults. In this paper, we present an induction motor fault detection and identification system using signal processing techniques such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A three-phase motor model is developed in MATLAB Simulink and simulated under various fault conditions. The current signature is observed using FFT, spectrogram, and scalogram to detect the faults. It is observed that under some fault conditions, the current signature analysis remains indistinguishable from the non-fault case. Therefore, deep learning (DL) methods are adopted here to identify these faults. The time-series data of healthy and unhealthy conditions are obtained from the simulation results. The comparative investigation among DL models confirmed the superiority of the long short-term memory (LSTM) model, which achieved 97.87% accuracy in identifying the individual faults.

Machine learning for fault analysis in rotating machinery: A comprehensive review

“…The most feature extraction techniques regarding FD, FDE, FI, or FP procedures of rotating machine components are statistical feature extraction (STFE) [ 10 , 27 , 29 , 101 ], Fast Fourier Transform (FFT) [ 8 , 33 , 34 , 109 ], Wavelet Transform (WWT) [ 20 , 73 , 78 , 134 ], and EMD [ 21 , 41 , 52 ]. There are also some methodologies derived from those techniques including Discrete Fourier Transform (DFT) [ 95 ], Short Time Fourier Transform (STFT) [ 36 ], Wavelet Packet Transform (WPT) [ 37 , 83 ], Continuous Wavelet Transform (CWT) [ 36 , 38 ], Discrete Wavelet Transform (DWT) [ 76 , 122 ], and Ensemble Empirical Mode Decomposition (EEMD) [ 112 , 121 ]. There are also other methods such as Singular Value Decomposition (SVD) [ 42 , 82 , 121 ], Similarity-Based Modelling (SBM) [ 100 ], and Hilbert Transform [ 35 , 42 ] that are all considered manual feature extraction techniques.…”

“…For this purpose, they deemed vibration [ 11 , 14 , 15 , 21 , 22 ], acoustic [ 11 , 23 , 24 ], thermal [ 13 ], current [ 6 , 7 , 9 , 25 , 26 ], pressure [ 27 ], and other characteristic data [ [27] , [28] , [29] ] as the main source for IFDP of rotating machines. Afterwards, the distinctive features are extracted by employing feature extraction methods such as statistical feature extraction [ [30] , [31] , [32] ], Fourier Transform [ [33] , [34] , [35] ], Wavelet Transform [ [36] , [37] , [38] ], Empirical Mode Decomposition [ 28 , 39 , 40 ] or other techniques [ 6 , 7 , 41 , 42 ]. The features may also be extracted automatically by employing deep learning approaches including convolutional neural networks, autoencoders, long-short term machines, etc.…”

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

“…Equipment Fault Parameters Method [40] Motor Bearing Current signal CNN [41] CNC machine Condition Vibrations ANN [42] Motor Operations Current and voltage signal ANN/MLP [43] Pump Condition Multi variables AE [44] CNC machine Mechanical Vibrations signal SAE [45] Motor Operations Stator currents ANN [46] Motor Bearing Vibrations signal LSTM [47] Rotating machinery Bearing Vibrations signal AE+ MLP [48] Rotating machinery Bearing Vibrations signal LSTM [49] Cooling radiator Condition Thermal image CNN [50] Rotating machinery Degradation image Infrared image streams (CNN+LSTM) (LSTM+AE) [51] Compressor Condition Multi variables RNN-LSTM [52] Elevator system Movement Acceleration data AE [53] Motor Condition Current signal EWT-CNN [54] Autoclave sterilizer Pump NTC thermistors LSTM [55] Worm gearboxes Operations Multi variables CNN [56] Rotating machinery Rotor, bearing Vibration signals CNN [57] Railcar factories Wheel bearing Temperature variation ANN [58] Rotating machinery Bearing Accelerometers CNN [59] Motor Bearing Current signal ANN [60] Conveyors system Motor Multi variables CNN [61] Motor Bearing Accelerometer LSTM+RNN [62] Motor Rotor bar Torque control ANN [63] Motor Stator winding stator currents ANN [64] Motor Condition Vibrations signal ANN [65] Rotating machinery Bearing Rotation speed, load levels CNN [66] Motor Stator winding Multi variables MLP+LSTM+CNN [67] Motor Operations Current signal ANN [68] Motor+rotating equipment Bearing Vibrations signal CNN+DNN [69] Motor Bearing Microphone, accelerometer DCNN+CNN-LSTM+LSTM…”

Incipient Inter-Turn Short Circuit Detection in Induction Motors Using Cumulative Distribution Function and the EfficientNetv2 Model

“…According to the above, the statistical features [7] and the wavelet technique for ITSC fault detection are widely applied due to the effectiveness of the signal extraction features in the time domain [8][9][10][11]. Besides that, approaches with Fast Fourier transforms (FFTs) have been implemented to search for spectral components related to the failure [12,13]. However, the challenge increases when the machine's natural behavior masks the failure, which is typically presented when the failure is incipient.…”