Intelligent Fault Diagnosis of Gearbox based on Multiple Synchrosqueezing S-Transform and Convolutional Neural Networks

doi:10.23940/ijpe.20.04.p4.528536

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (4): 528-536.doi: 10.23940/ijpe.20.04.p4.528536

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Intelligent Fault Diagnosis of Gearbox based on Multiple Synchrosqueezing S-Transform and Convolutional Neural Networks

Meng Shuo^*, Jianshe Kang, Kuo Chi, and Xupeng Die

Shijiazhuang Branch, Army Engineering University, Shijiazhuang, 050003, China

Submitted on ; Revised on ; Accepted on
Contact: Meng Shuo
About author:
Shuo Meng received his B.Sc. degree from the Army Engineering University of PLA, Shijiazhuang, China in 2018. He is currently a full-time M.S. student at the Army Engineering University of PLA. His current research is focused on mechanical fault detection, intelligent diagnosis, and prognostics.
Jianshe Kang received his Ph.D. in mechatronical engineering from the Beijing Institute of Technology, Beijing, China. He is a professor at the Army Engineering University of PLA. He is a direct general of China Ordinance Industry Society and was selected to be part of the editorial board of Acta Armamentarill. His current research interests include system reliability analysis, condition-based prognostics, and health management of capital assets.
Kuo Chi received his B.Sc. degree in mechanical engineering from Jimei University, Xiamen, China in 2013 and his M.S. degree in maintenance engineering from Mechanical Engineering College, Shijiazhuang, China in 2015. He is currently a full-time Ph.D. student at the Army Engineering University of PLA. His current research is focused on mechanical fault detection, diagnostics, and prognostics.
Xupeng Die received his B.Sc. degree in maintenance engineering from Mechanical Engineering College in 2017 and his M.S. degree from the Army Engineering University of PLA in 2019. His current research interests include mechanical fault feature extraction and fault diagnosis.

Abstract

Abstract: In order to solve the problem of gearbox fault diagnosis, we proposed a gearbox fault diagnosis method based on multiple synchrosqueezing S-transform (MSSST) and convolutional neural network (CNN). Firstly, the time-frequency analysis method of MSSST is used to solve the problem that general time-frequency analysis methods cannot obtain good time-frequency aggregation when processing the strong time-varying signals. Then, convolutional neural network is used to extract the image features of time-frequency graphs and classify the faults to diagnosis. Finally, we set up the combination of different time-frequency analysis methods and different deep learning models to compare and analyze the advantages and disadvantages among different methods and verify the effectiveness of the MSSST and CNN methods. The test results show that the fault diagnosis method based on MSSST and CNN has the highest accuracy and shortest operation time compared with other methods, which verifies the effectiveness and superiority of this method.

Key words: multiple synchrosqueezing S-transform, convolutional neural network, time-frequency analysis, deep learning, fault diagnosis

Shuo Meng, Jianshe Kang, Kuo Chi, and Xupeng Die. Intelligent Fault Diagnosis of Gearbox based on Multiple Synchrosqueezing S-Transform and Convolutional Neural Networks [J]. Int J Performability Eng, 2020, 16(4): 528-536.

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Intelligent Fault Diagnosis of Gearbox based on Multiple Synchrosqueezing S-Transform and Convolutional Neural Networks

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