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An Open-switch Fault Diagnosis Method for Single-phase PWM Rectifier Based on CEEMD-DNN

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  • Intelligent Control and Applications
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Abstract

Based on complementary ensemble empirical mode decomposition and deep neural network (CEEMD-DNN), a novel diagnosis method is proposed to discover the open-circuit faults of insulated gate bipolar transistors (IGBTs) in single-phase pulse width modulation (PWM) AC-DC rectifier, an important part of traction power supply system of high-speed railway. By virtue of the combination of signal processing and deep learning schemes, the multi-scale feature information for IGBT’s open-circuit fault diagnosis are extracted by regarding the input current of rectifier as the original signal. More clearly, CEEMD is adopted to decompose the original signal to a series of intrinsic mode function components (IMFs). Then, the correlation coefficient algorithm is used to evaluate the correlation between each of the IMFs and the original signal. Further, those IMFs that have been verified to be highly correlated with the original signal are selected as the input eigenvectors of DNN to train the IGBTs fault diagnosis network. Experimental results show that the proposed CEEMD-DNN algorithm is superior to pure DNN, empirical mode decomposition (EMD)-DNN and ensemble empirical mode decomposition (EEMD)-DNN in the sense that within the similar time-consuming situations the fault diagnosis accuracy has been improved from 73.0%, 86.0%, and 89.9% to almost 99.0%. This is mainly owing to the remarkable decomposing performance of CEEMD for complex mixed signal, and the strong feature extraction and learning abilities of DNN in pattern recognition.

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Abbreviations

EMD:

Empirical mode decomposition

EEMD:

Ensemble empirical mode decomposition

CEEMD:

Complementary ensemble empirical mode decomposition

DNN:

Deep neural network

RNN:

Recurrent neural network

CNN:

Convolutional neural network

CRNN:

Convolutional recurrent neural network

Densenet:

Dense convolutional network

IGBT:

Inulated gate bipolar transistors

PWM:

Pulse width modulation

IMF:

Intrinsic mode function

FFT:

Fast Fourier transform

STFT:

Short-time Fouriertransform

WT:

Wavelet transform

DSP:

Digital signal processor

T-SNE:

T-distributed stochastic neighbor embedding

PET:

Power electronic transformer

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Authors and Affiliations

  1. Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Na Qin, Tianwei Wang, Deqing Huang, Yiming Zhang & Lei Ma

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  1. Na Qin
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  2. Tianwei Wang
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Correspondence to Deqing Huang.

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The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

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This work was supported by Natural Science Foundation of China(62173279, 61733015, U1934221, 61773323) and the Central Universities Pay for Basic Scientific Research (2682021ZTPY027).

Na Qin received her B.S. degree in electrical technology from the School of Electrical Engineering, Zhengzhou University, Zhengzhou, China, in 2000, an M.S. degree in electric power system and automation, and a Ph.D. degree in electrical engineering from the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China, in 2003 and 2014, respectively. She is currently an Associate Professor with the School of Electrical Engineering, Southwest Jiaotong University. Her research interests include intelligent information processing, fault diagnosis, pattern recognition, and intelligent systems.

Tianwei Wang received his B.Eng. degree in electrical engineering from Southwest Jiaotong University in 2021. He is currently working toward an M.S. degree in control science and engineering at Southwest Jiaotong University, Chengdu, China. His researh interests include fault diagnosis, fault tolerant control of rectifier, and intelligent information processing.

Deqing Huang received his B.S. and Ph.D. degrees in applied mathematics from the Mathematical College, Sichuan University, Chengdu, China, in 2002 and 2007, respectively. He received the second Ph.D. degree in control engineering from National University of Singapore (NUS). From 2013 to 2016, he was a Research Associate with the Department of Aeronautics, Imperial College London, London, UK. In January 2016, he joined the Department of Electronic and Information Engineering, Southwest Jiaotong Univerisity, Chengdu, China as a Professor and the Department Head. His research interests lie in the areas of modern control theory, fluid analysis and control, convex optimization, and robotics.

Yiming Zhang received his B.S. degree from the Lanzhou Jiaotong University, Lanzhou, China in 2019. He is currently working towards a Ph.D. degree in the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China. His research interests lie in the areas of fault diagnosis and deep learning.

Lei Ma received his B.Eng. degree in automatic control from Chongqing University in 1993, an M.S. degree in electric drive from the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China, and a Ph.D. degree in electrical engineering from Ruhr-Universität Bochum. He is currently a Professor with the School of Electrical Engineering, Southwest Jiaotong University. His research interests include robot control, new energy system control, and high speed train service safety monitoring and evaluation.

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Qin, N., Wang, T., Huang, D. et al. An Open-switch Fault Diagnosis Method for Single-phase PWM Rectifier Based on CEEMD-DNN. Int. J. Control Autom. Syst. 21, 3430–3442 (2023). https://doi.org/10.1007/s12555-021-0905-3

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