Analysis of Permanent Magnet Synchronous Motor Fault Diagnosis Based on Learning

This paper presents an effective diagnosis algorithm for permanent magnet synchronous motors running with an array of faults of varying severity over a wide speed range. The fault diagnosis is based on a current signature analysis. The complete fault motor diagnosis system requires the extraction of features based on the current method and a subsequent method for adding classifications. In this paper, we propose two feature extraction methods: the first involves a classification method that utilizes a wavelet packet transform and the second is a deep 1-D convolution neural network that includes a softmax layer. The experimental results obtained using real-time motor stator current data demonstrate the effectiveness of the proposed methods for real-time monitoring of motor conditions. The results also demonstrate that the proposed methods can effectively diagnose five different motor states, including two different demagnetization fault states and two bearing fault states.

[1]  Merugu Siva Rama Krishna,et al.  Fault diagnosis of induction motor using Motor Current Signature Analysis , 2013, 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT).

[2]  Ruqiang Yan,et al.  Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[3]  Qing-Guo Wang,et al.  Fuzzy-Model-Based Fault Detection for a Class of Nonlinear Systems With Networked Measurements , 2013, IEEE Transactions on Instrumentation and Measurement.

[4]  Dan Zhang,et al.  Distributed fault detection for a class of large-scale systems with multiple incomplete measurements , 2015, J. Frankl. Inst..

[5]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[6]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[7]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  J. Rosero,et al.  Simulation and Fault Detection of Short Circuit Winding in a Permanent Magnet Synchronous Machine (PMSM) by means of Fourier and Wavelet Transform , 2008, 2008 IEEE Instrumentation and Measurement Technology Conference.

[9]  K. M. Bhurchandi,et al.  Classification of Mental Task Based on EEG Processing Using Self Organising Feature Map , 2014, 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics.

[10]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[11]  K. I. Ramachandran,et al.  Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing , 2007 .

[12]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[13]  Bo-Suk Yang,et al.  Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference , 2009, Expert Syst. Appl..

[14]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Simon Lacroix,et al.  Classification of Outdoor 3D Lidar Data Based on Unsupervised Gaussian Mixture Models , 2017, IEEE Transactions on Automation Science and Engineering.

[16]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[17]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[18]  Selin Aviyente,et al.  Time–Frequency Analysis for Efficient Fault Diagnosis and Failure Prognosis for Interior Permanent-Magnet AC Motors , 2008, IEEE Transactions on Industrial Electronics.

[19]  Yongchun Liang,et al.  Diagnosis of inter-turn short-circuit stator winding fault in PMSM based on stator current and noise , 2014, 2014 IEEE International Conference on Industrial Technology (ICIT).

[20]  M. Azizur Rahman,et al.  Development and Implementation of a Novel Fault Diagnostic and Protection Technique for IPM Motor Drives , 2009, IEEE Transactions on Industrial Electronics.

[21]  Ole-Christoffer Granmo,et al.  Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[22]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using machine learning methods , 2011, Expert Syst. Appl..

[23]  T.G. Habetler,et al.  Motor bearing damage detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[24]  Mohamed Benbouzid,et al.  A review of induction motors signature analysis as a medium for faults detection , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[25]  Dariusz Głas,et al.  Optimization of an FPGA Trigger Based on an Artificial Neural Network for the Detection of Neutrino-Induced Air Showers , 2017, IEEE Transactions on Nuclear Science.

[26]  Thomas G. Habetler,et al.  An amplitude modulation detector for fault diagnosis in rolling element bearings , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[27]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[28]  H. Harry Asada,et al.  Implicit and Intuitive Grasp Posture Control for Wearable Robotic Fingers: A Data-Driven Method Using Partial Least Squares , 2016, IEEE Transactions on Robotics.

[29]  M. Riera-Guasp,et al.  A Critical Comparison Between DWT and Hilbert–Huang-Based Methods for the Diagnosis of Rotor Bar Failures in Induction Machines , 2009, IEEE Transactions on Industry Applications.

[30]  L. Romeral,et al.  Detection of Demagnetization Faults in Permanent-Magnet Synchronous Motors Under Nonstationary Conditions , 2009, IEEE Transactions on Magnetics.

[31]  Tie Qiu,et al.  Remote Sensing Image Classification Based on Ensemble Extreme Learning Machine With Stacked Autoencoder , 2017, IEEE Access.

[32]  Teresa Orlowska-Kowalska,et al.  Neural networks application for induction motor faults diagnosis , 2003, Math. Comput. Simul..

[33]  Jian Yang,et al.  ISAR imaging of targets with rotating parts based on robust principal component analysis , 2017 .

[34]  Lipika Dey,et al.  A k-mean clustering algorithm for mixed numeric and categorical data , 2007, Data Knowl. Eng..

[35]  Insoo Koo,et al.  Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features , 2017, IEEE Access.

[36]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[37]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[38]  Yunjian Jia,et al.  Passenger flow estimation based on convolutional neural network in public transportation system , 2017, Knowl. Based Syst..

[39]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[40]  Zhiwei Gao,et al.  From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis , 2013, IEEE Transactions on Industrial Informatics.

[41]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.