Induction Motor Bearing Fault Classification Using Extreme Learning Machine Based on Power Features

Electric motors perform the crucial task of converting electrical energy into essential mechanical energy on demand. Motors are plentifully used in the industrial sector all over the world to drive mechanical appliances. Despite being robust and sturdy, motors are not entirely fault-proof, and faults that are caused by the bearings trouble them the most. Early detection of these faults allows engineers to take preventive measures and avert hard breakdowns. Numerous studies have been conducted in this area of research. Many methods have been proposed and implemented to detect the existence and determine the type of fault present in an induction motor. However, this field of research is still open since there is room for improvements in the claimed results. In this paper, a novel fault diagnosis method has been proposed involving an emerging machine learning technique named extreme learning machine to identify the existence of flaws in motor bearings and specify their origins. The described method is tested on a benchmark bearing fault dataset provided by Case Western Reserve University Bearing Data Center. The acquired result yields a maximum classification accuracy of 99.86% and an average classification accuracy of 98.67% after being tested on multiple fault datasets.

[1]  Haidong Shao,et al.  Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine , 2018, Knowl. Based Syst..

[2]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[3]  Frank L. Vernon,et al.  Multitaper spectral analysis of high-frequency seismograms , 1987 .

[4]  Juan Carlos Fernández,et al.  Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms , 2014, Ann. Oper. Res..

[5]  David Zhang,et al.  Domain Adaptation Guided Drift Compensation , 2018 .

[6]  Meng Luo,et al.  Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings. , 2016, ISA transactions.

[7]  Xiaodong Wang,et al.  Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine , 2018 .

[8]  Niloy Sikder,et al.  Fault Diagnosis of Motor Bearing Using Ensemble Learning Algorithm with FFT-based Preprocessing , 2019, 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST).

[9]  Fucai Li,et al.  A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine , 2019, Neurocomputing.

[10]  Rene de Jesus Romero-Troncoso,et al.  FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors , 2012 .

[11]  M. Johnson,et al.  Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.

[12]  Guiji Tang,et al.  A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy , 2016 .

[13]  Wenlong Li,et al.  Fault Diagnosis of Motor Bearings Based on a One-Dimensional Fusion Neural Network , 2019, Sensors.

[14]  Wentao Mao,et al.  A novel deep output kernel learning method for bearing fault structural diagnosis , 2019, Mechanical Systems and Signal Processing.

[15]  Mark D. McDonnell,et al.  Modular expansion of the hidden layer in Single Layer Feedforward neural Networks , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[16]  Abdullah Al Nahid,et al.  Fault Diagnosis of Induction Motor Bearing Using Cepstrum-based Preprocessing and Ensemble Learning Algorithm , 2019, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).

[17]  Qin Hu,et al.  Fault Diagnosis Based on Weighted Extreme Learning Machine With Wavelet Packet Decomposition and KPCA , 2018, IEEE Sensors Journal.

[18]  A. Walden,et al.  Spectral analysis for physical applications : multitaper and conventional univariate techniques , 1996 .

[19]  Chee Peng Lim,et al.  Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models , 2014, Expert Syst. Appl..

[20]  Chandrabhanu Malla,et al.  Review of Condition Monitoring of Rolling Element Bearing Using Vibration Analysis and Other Techniques , 2019, Journal of Vibration Engineering & Technologies.

[21]  Zhipeng Wang,et al.  Adaptive Diagnosis for Rotating Machineries Using Information Geometrical Kernel-ELM Based on VMD-SVD , 2018, Entropy.

[22]  Weiming Shen,et al.  Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning , 2019, Sensors.

[23]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[24]  Serkan Kiranyaz,et al.  A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier , 2018, Journal of Signal Processing Systems.

[25]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[26]  Donald B. Percival,et al.  Spectral Analysis for Physical Applications , 1993 .

[27]  Xiaodong Wang,et al.  Fault diagnosis of rolling bearing based on permutation entropy and Extreme Learning Machine , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[28]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[29]  Samarjit Sengupta,et al.  Induction Motor Fault Diagnosis , 2016 .

[30]  Girish Kumar Singh,et al.  Induction machine drive condition monitoring and diagnostic research—a survey , 2003 .

[31]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

[33]  Pablo Alvarez,et al.  Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis , 2019, Entropy.

[34]  Min Yao,et al.  A Fast Incremental Method Based on Regularized Extreme Learning Machine , 2015 .

[35]  Douglas G. Martinson,et al.  Quantitative Methods of Data Analysis for the Physical Sciences and Engineering , 2018 .

[36]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[37]  Mark D. McDonnell,et al.  Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm , 2015, PloS one.

[38]  Jian Ma,et al.  Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine , 2015 .

[39]  Wang Jiangjiang,et al.  Spectral quantitative analysis of complex samples based on the extreme learning machine , 2016 .

[40]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[41]  Bernardo Spagnolo,et al.  Nonlinear Relaxation Phenomena in Metastable Condensed Matter Systems , 2016, Entropy.

[42]  Nibaldo Rodríguez,et al.  Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis , 2017, Entropy.

[43]  Enrico Zio,et al.  Adaptive incremental ensemble of extreme learning machines for fault diagnosis in induction motors , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[44]  Jong-Myon Kim,et al.  Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network , 2019, Comput. Ind..

[45]  Rajesh Purohit,et al.  Vibration Analysis & Condition Monitoring for Rotating Machines: A Review , 2017 .

[46]  Han Xiao,et al.  Multi-fault classification based on the two-stage evolutionary extreme learning machine and improved artificial bee colony algorithm , 2014 .

[47]  Xiang Li,et al.  Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks , 2019, IEEE Transactions on Industrial Electronics.

[48]  Guowu Yang,et al.  Modeling, Optimization, and Verification for Complex Systems , 2016 .

[49]  Yuan Li,et al.  Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study , 2017 .

[50]  Abdelkrim Moussaoui,et al.  A Comparative Study of Various Methods of Bearing Faults Diagnosis Using the Case Western Reserve University Data , 2016, Journal of Failure Analysis and Prevention.