Traction Inverter Open Switch Fault Diagnosis Based on Choi-Williams Distribution Spectral Kurtosis and Wavelet-Packet Energy Shannon Entropy

In this paper, a new approach for fault detection and location of open switch faults in the closed-loop inverter fed vector controlled drives of Electric Multiple Units is proposed. Spectral kurtosis (SK) based on Choi–Williams distribution (CWD) as a statistical tool can effectively indicate the presence of transients and locations in the frequency domain. Wavelet-packet energy Shannon entropy (WPESE) is appropriate for the transient changes detection of complex non-linear and non-stationary signals. Based on the analyses of currents in normal and fault conditions, SK based on CWD and WPESE are combined with the DC component method. SK based on CWD and WPESE are used for the fault detection, and the DC component method is used for the fault localization. This approach can diagnose the specific locations of faulty Insulated Gate Bipolar Transistors (IGBTs) with high accuracy, and it requires no additional devices. Experiments on the RT-LAB platform are carried out and the experimental results verify the feasibility and effectiveness of the diagnosis method.

[1]  Jin Zhao,et al.  High-Performance Fault Diagnosis in PWM Voltage-Source Inverters for Vector-Controlled Induction Motor Drives , 2014, IEEE Transactions on Power Electronics.

[2]  Peter W. Tse,et al.  An enhanced Kurtogram method for fault diagnosis of rolling element bearings , 2013 .

[3]  Dong Wang,et al.  Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients , 2017 .

[4]  Daniel U. Campos-Delgado,et al.  An Observer-Based Diagnosis Scheme for Single and Simultaneous Open-Switch Faults in Induction Motor Drives , 2011, IEEE Transactions on Industrial Electronics.

[5]  Jorge O. Estima,et al.  A new approach for real-time multiple open-circuit fault diagnosis in voltage source inverters , 2010 .

[6]  Song Zhe.  A Diagnosis Method for Inverter Open-circuit Faults of Brushless DC Motor Driver Systems , 2013 .

[7]  Makarand Sudhakar Ballal,et al.  Multi-resolution analysis for converter switch faults identification , 2015 .

[8]  Li Sun,et al.  Switching Function Model-Based Fast-Diagnostic Method of Open-Switch Faults in Inverters Without Sensors , 2011, IEEE Transactions on Power Electronics.

[9]  Zhigang Liu,et al.  A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM , 2015, IEEE Transactions on Smart Grid.

[10]  Arcangelo Pellegrino,et al.  Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems , 2017, Entropy.

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

[12]  Arcangelo Pellegrino,et al.  Entropic Measure of Epistemic Uncertainties in Multibody System Models by Axiomatic Design , 2017, Entropy.

[13]  Zhu Lin.  Recognition of transient power quality disturbances based on CWD spectral kurtosis , 2014 .

[14]  Zhigang Liu,et al.  Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways , 2016, Entropy.

[15]  Dawei Xiang,et al.  An Industry-Based Survey of Reliability in Power Electronic Converters , 2011, IEEE Transactions on Industry Applications.

[16]  Dong-Choon Lee,et al.  Fault diagnosis of three-phase PWM inverters using wavelet and SVM , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[17]  Yang Zhang,et al.  Multiwavelet Packet Entropy and its Application in Transmission Line Fault Recognition and Classification , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Mounira Berkani,et al.  Ageing and Failure Modes of IGBT Modules in High-Temperature Power Cycling , 2011, IEEE Transactions on Industrial Electronics.

[19]  Dong Wang,et al.  An extension of the infograms to novel Bayesian inference for bearing fault feature identification , 2016 .

[20]  Roger F. Dwyer,et al.  Detection of non-Gaussian signals by frequency domain Kurtosis estimation , 1983, ICASSP.

[21]  M. Aktas,et al.  Wavelet-based switching faults detection in direct torque control induction motor drives , 2010 .

[22]  Francesco Villecco,et al.  Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis , 2017, Entropy.

[23]  Antonio Marcus Nogueira Lima,et al.  Fault detection of open-switch damage in voltage-fed PWM motor drive systems , 2003 .

[24]  A. J. M. Cardoso,et al.  Recent advances in fault diagnosis by Park's vector approach , 2013, 2013 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD).

[25]  Feng Jia,et al.  Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution , 2015, Sensors.

[26]  Sauro Longhi,et al.  Electric motor defects diagnosis based on kernel density estimation and Kullback-Leibler divergence in quality control scenario , 2015, Eng. Appl. Artif. Intell..

[27]  Mateu Sbert,et al.  Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools , 2017, Entropy.

[28]  Zhigang Liu,et al.  Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform , 2015, Entropy.

[29]  Zhigang Liu,et al.  An Approach to Recognize the Transient Disturbances With Spectral Kurtosis , 2014, IEEE Transactions on Instrumentation and Measurement.