Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system.

[1]  Dianguo Xu,et al.  Motor Speed Signature Analysis for Local Bearing Fault Detection With Noise Cancellation Based on Improved Drive Algorithm , 2020, IEEE Transactions on Industrial Electronics.

[3]  M. A. Abido,et al.  Neural Network-Based Diagnostic Tool for Detecting Stator Inter-Turn Faults in Line Start Permanent Magnet Synchronous Motors , 2019, IEEE Access.

[4]  Jian-Xin Shen,et al.  Investigation and Countermeasures for Demagnetization in Line Start Permanent Magnet Synchronous Motors , 2013, IEEE Transactions on Magnetics.

[5]  Siyuan Liang,et al.  Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal , 2018, Applied Sciences.

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

[7]  M. A. Abido,et al.  An Accurate Tool for Detecting Stator Inter-Turn Fault in LSPMSM , 2019, IEEE Access.

[8]  Antonio J. Marques Cardoso,et al.  On-Line Stator Fault Diagnosis in Line-Start Permanent Magnet Synchronous Motors , 2018, 2018 XIII International Conference on Electrical Machines (ICEM).

[9]  Jose A. Antonino-Daviu,et al.  Case stories of induction motors fault diagnosis based on current analysis , 2016, 2016 Petroleum and Chemical Industry Conference Europe (PCIC Europe).

[10]  Aswin Uvaraj Ganesan,et al.  Review on the evolution of technology advancements and applications of line-start synchronous machines , 2019 .

[11]  Fernando J. T. E. Ferreira,et al.  Technical and Economical Considerations on Super High-Efficiency Three-Phase Motors , 2012, IEEE Transactions on Industry Applications.

[12]  Wei Zhang,et al.  A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals , 2017, Sensors.

[13]  N. Yassa,et al.  Motor Current Signature Analysis for the Air Gap Eccentricity Detection In the Squirrel Cage Induction Machines , 2019, Energy Procedia.

[14]  Adam Glowacz,et al.  Acoustic based fault diagnosis of three-phase induction motor , 2018, Applied Acoustics.

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

[16]  Mehmet Akar,et al.  Design of A Novel Line Start Synchronous Motor Rotor , 2018 .

[17]  Yibin Li,et al.  Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis , 2019, Journal of Signal Processing Systems.

[18]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[19]  Adam Glowacz,et al.  Fault diagnosis of single-phase induction motor based on acoustic signals , 2019, Mechanical Systems and Signal Processing.

[20]  Keiron O'Shea,et al.  An Introduction to Convolutional Neural Networks , 2015, ArXiv.

[21]  Sang Woo Kim,et al.  Classification and Detection of Demagnetization and Inter-Turn Short Circuit Faults in IPMSMs by Using Convolutional Neural Networks , 2018, 2018 IEEE Energy Conversion Congress and Exposition (ECCE).

[22]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[23]  Aurobinda Routray,et al.  Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length , 2017, IEEE Transactions on Instrumentation and Measurement.

[24]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[25]  Jeevanand Seshadrinath,et al.  Incipient Inter-turn Fault Diagnosis in Induction motors using CNN and LSTM based Methods , 2018, 2018 IEEE Industry Applications Society Annual Meeting (IAS).

[26]  Jin Hur,et al.  Detection of Inter-Turn and Dynamic Eccentricity Faults Using Stator Current Frequency Pattern in IPM-Type BLDC Motors , 2016, IEEE Transactions on Industrial Electronics.

[27]  Yonghong Zhang,et al.  Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network , 2017, Chinese Journal of Mechanical Engineering.

[28]  Jos Knockaert,et al.  Comparing MCSA with vibration analysis in order to detect bearing faults — A case study , 2015, 2015 IEEE International Electric Machines & Drives Conference (IEMDC).

[29]  Luo Yingli,et al.  Dynamic Irreversible Demagnetization Behavior of Line-start Permanent Magnet Synchronous Motors in the Starting Process , 2015 .

[30]  Yu-Min Hsueh,et al.  Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform , 2019, Symmetry.

[31]  Davood Arab Khaburi,et al.  ANN based fault diagnosis of permanent magnet synchronous motor under stator winding shorted turn , 2015 .

[32]  C. Kral,et al.  Detection of mechanical imbalances of induction machines without spectral analysis of time-domain signals , 2004, IEEE Transactions on Industry Applications.

[33]  Bhim Singh,et al.  Investigation of Vibration Signatures for Multiple Fault Diagnosis in Variable Frequency Drives Using Complex Wavelets , 2014, IEEE Transactions on Power Electronics.

[34]  Mohamed Moustafa Mahmoud Sedky,et al.  Diagnosis of Static, Dynamic and Mixed Eccentricity in Line Start Permanent Magnet Synchronous Motor by Using FEM , 2014 .

[35]  N. Rama Devi,et al.  Diagnosis and classification of stator winding insulation faults on a three-phase induction motor using wavelet and MNN , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

[36]  Alessandro Goedtel,et al.  Stator fault analysis of three-phase induction motors using information measures and artificial neural networks , 2017 .

[37]  Dubravko Miljković,et al.  Brief Review of Motor Current Signature Analysis , 2015 .

[38]  Turker Ince,et al.  Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks , 2019, Electrical Engineering.

[39]  Andrew Ball,et al.  A Comparison of Different Techniques for Induction Motor Rotor Fault Diagnosis , 2012 .

[40]  Mohammad Ali Abido,et al.  Mathematical Modeling, Simulation and Experimental Testing of Interior-Mount LSPMSM Under Stator Inter-Turn Fault , 2019, IEEE Transactions on Energy Conversion.

[41]  Zafer Dogan.  The Static Eccentricity Fault Diagnosis in Time Domain at Line Start Permanent Magnet Synchronous Motor , 2016 .

[42]  Levent Eren,et al.  Broken rotor bar detection via four-band wavelet packet decomposition of motor current , 2018 .

[43]  Norman Mariun,et al.  Wavelet-Based Analysis of MCSA for Fault Detection in Electrical Machine , 2015 .