scholarly journals Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

2020 ◽  
Vol 10 (15) ◽  
pp. 5251 ◽  
Author(s):  
Rafia Nishat Toma ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motors ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Current Signal ◽  
Bearing Fault ◽  
Ensemble Machine Learning

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

scholarly journals Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor

2020 ◽  
Vol 24 (3) ◽  
pp. 417-424
Author(s):  
A.I. Abdullateef ◽  
O.S. Fagbolagun ◽  
M.F. Sanusi ◽  
M.F. Akorede ◽  
M.A. Afolayan
Keyword(s):  
Wavelet Transform ◽  
Induction Motor ◽  
Euclidean Distance ◽  
Induction Motors ◽  
Short Circuit ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Machine Failure ◽  
Wavelet Energy

Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and  classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on  wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform

MOBILE ROBOT MOTOR BEARING FAULT DETECTION AND CLASSIFICATION ON DISCRETE WAVELET TRANSFORM AND LSTM NETWORK

2018 ◽  
Vol 18 (08) ◽  
pp. 1840034 ◽  
Author(s):  
SHIWEI LI ◽  
YONGPING ZHAO ◽  
MINGLI DING
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Mobile Robot ◽  
Discrete Wavelet Transform ◽  
Vibration Signal ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Bearing Fault ◽  
Fault Type ◽  
Motor Bearing

The impact of motors breakdown and failures on mobile robot motor bearing is an important concern for robot industries. For this reason, predictive motor lifetime and bearing fault classification techniques are being investigated extensively as a method of decreasing motor downtime and enhancing mobile robot reliability. With increasing attention on neural network technologies, many researchers have carried out lots of the relevant experiments and analyses, very plentiful and important conclusions are obtained. In this article, a classification method based on discrete wavelet transform (DWT) and long short-term memory network (LSTM) a proposed to find and classify fault type of mobile robot permanent magnet synchronous motor (PMSM). First, a set of mobile robot motor vibration signal were collected by the sensors. Second, the obtained vibration signal is decomposed into six frequency bands by the DWT. Haar function is selected as the mother function in the processing. The energy of every frequency band was calculated as a classification feature. Thirdly, four classification features with high classification rate are obtained. The feature vector is used as input of the neural network, and the fault type is identified by LSTM classifier with deviation unit. From the results of the experiments provided in the paper, the method can detect the fault type accurately and it is feasible and effective under different motor speed.

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