A Wavelet-Based Fault Diagnosis Approach for Permanent Magnet Synchronous Motors

This paper presents a generic analytical tool to diagnose and classify faults in permanent magnet synchronous motors. The proposed method deploys wavelet transform to extract features for fault diagnosis, which provides a framework for studying nonstationary trends. Analyzing the stator current with wavelet raises a challenge since the energy of fundamental component spreads over different scales in the decomposition. An adaptive filter is used to estimate and remove the fundamental component in stator current. This filter predicts the main harmonic by processing the measured current data in real-time without any speed feedback. The proposed filter is designed in a way that it does not affect or suppress fault related harmonics. The estimation accuracy and convergence rate of this filter is tested and reported by error bounds, which exhibit an acceptable robustness. The validity of the proposed fault diagnosis approach is verified by finite element simulations and experimental results. The effectiveness of this algorithm is tested using two case studies including broken magnet and eccentricity faults. An average accuracy above 96% is obtained using experimental and simulation data. It is proven that the filtering scheme increases the overall accuracy of fault diagnosis.

[1]  Shahin Hedayati Kia,et al.  Gear Tooth Surface Damage Fault Detection Using Induction Machine Stator Current Space Vector Analysis , 2015, IEEE Transactions on Industrial Electronics.

[2]  Wei Zhang,et al.  Dual-Impulse Response Model for the Acoustic Emission Produced by a Spall and the Size Evaluation in Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.

[3]  Myeongsu Kang,et al.  An FPGA-Based Multicore System for Real-Time Bearing Fault Diagnosis Using Ultrasampling Rate AE Signals , 2015, IEEE Transactions on Industrial Electronics.

[4]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[5]  Mehrdad Heydarzadeh,et al.  A Robust Feature Extraction for Automatic Fault Diagnosis of Rolling Bearings Using Vibration Signals , 2017 .

[6]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[7]  Antoine Picot,et al.  Current-Based Detection of Mechanical Unbalance in an Induction Machine Using Spectral Kurtosis With Reference , 2015, IEEE Transactions on Industrial Electronics.

[8]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[9]  Gérard-André Capolino,et al.  Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art , 2015, IEEE Transactions on Industrial Electronics.

[10]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[11]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[12]  Seungdeog Choi,et al.  A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive-Embedded Fault Diagnosis Systems , 2011, IEEE Transactions on Industrial Electronics.

[13]  Lorand Szabo,et al.  Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement , 2015, IEEE Transactions on Industrial Electronics.

[14]  Bijaya K. Panigrahi,et al.  Vibration Analysis Based Interturn Fault Diagnosis in Induction Machines , 2014, IEEE Transactions on Industrial Informatics.

[15]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[16]  Jose A. Antonino-Daviu,et al.  A General Approach for the Transient Detection of Slip-Dependent Fault Components Based on the Discrete Wavelet Transform , 2008, IEEE Transactions on Industrial Electronics.

[17]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[18]  P. Bauer,et al.  Detection of eccentricity and bearings fault using stray flux monitoring , 2011, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives.

[19]  Chrysostomos D. Stylios,et al.  An automated thermographic image segmentation method for induction motor fault diagnosis , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[20]  Bo-Suk Yang,et al.  Application of Self Organizing Map for Intelligent Machine Fault Diagnostics Based on Infrared Thermography Images , 2011, 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications.

[21]  Jose A. Antonino-Daviu,et al.  Outer race bearing fault detection in induction machines using stator current signals , 2015, 2015 IEEE 13th International Conference on Industrial Informatics (INDIN).

[22]  Shahin Hedayati Kia,et al.  Gear fault diagnosis using discrete wavelet transform and deep neural networks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[23]  Mehrdad Heydarzadeh,et al.  A Two-Stage Fault Detection and Isolation Platform for Industrial Systems Using Residual Evaluation , 2016, IEEE Transactions on Instrumentation and Measurement.

[24]  Hee-Dong Kim,et al.  Identification of false rotor fault indications produced by on-line MCSA for medium voltage induction machines , 2015, 2015 61st IEEE Pulp and Paper Industry Conference (PPIC).

[25]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[26]  Alberto Bellini,et al.  Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals , 2009, IEEE Transactions on Industrial Electronics.

[27]  Jawad Faiz,et al.  Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis , 2014, IEEE Transactions on Industrial Electronics.

[28]  Humberto Henao,et al.  Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques , 2014, IEEE Industrial Electronics Magazine.

[29]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

[30]  Erik Leandro Bonaldi,et al.  Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current , 2015, IEEE Transactions on Industrial Electronics.