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

• Orginal Article • Previous Articles Next Articles

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.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 15.

1.	P. Wang, T. Y. Li, X. J. Gao, et al., “Noise Reduction Method for Fault Signal of Layered Adaptive Wavelet Threshold Bearing,” Journal of Vibration Engineering, Vol. 32, No. 3, pp. 548-556, 2019
2.	X. Y. Zhu, Y. J. Wang, Y. Q. Zhang,J. Y. Yuan, “Fault Diagnosis of Rolling Bearing based on Adaptive Optimal Morlet Wavelet,” Journal of Vibration, Testing and Diagnosis, Vol. 38, No. 5, pp. 1021-1029, 2018
3.	C. Mishra, A. K. Samantaray,G. Chakraborty, “Rolling Element Bearing Fault Diagnosis under Slow Speed Operation using Wavelet De-Noising,”Measurement, Vol. 103, pp. 77-86, 2017
4.	S. Hussain and H. A. Gabbar, “Fault Diagnosis in Gearbox using Adaptive Wavelet Filtering and Shock Response Spectrum Features Extraction,” Structural Health Monitoring-an International Journal, Vol. 12, No. 2, pp. 169-180, 2013
5.	Y. G. Xu, G. L. Zhao, Z. Y. Ma, et al., “Application of Shuangshufu Wavelet and Local Projection Algorithm in Gear Fault Diagnosis,” Journal of Vibration Engineering, Vol. 28, No. 4, pp. 650-656, 2015
6.	I. Daubechies, “TenLecturesonWavelets,” National Defense Industry Press, 2011
7.	Z. L.Huang and J. Z. Zhang, “Synchrosqueezing S-Transform,” Scientia Sinica, No. 5, 2016
8.	G. Yu, M. J. Yu,C. Y. Xu, “Synchroextracting Transform,” IEEE Transactions on Industrial Electronics, Vol. 1, No. 1, pp. 99, 2017
9.	Q. Wang, J. Gao, N. Liu,X. Jiang, “High-Resolution Seismic Time-Frequency Analysis using the Synchrosqueezing Generalized S-Transform,” IEEE Geoscience and Remote Sensing Letters, Vol. 1, No. 5, pp. 99, 2018
10.	H. Ren, J. -F. Qu, Y. Chai, Q. Tang, and X. Ye, “Deep Learning for Fault Diagnosis: The State of the Art and Challenge,” Control and Decision-Making, Vol. 32, No. 8, pp. 1345-1358, 2017
11.	Y. C.Wang and X. S. Duan, “Research Progress of Deep Learning in Equipment Fault Diagnosis,” Tactical Missile Technology, Vol. 191, No. 5, pp. 31-36, 2018
12.	M. B. Zhang, L. W. Wang,Y. Q. Feng, “OFDM Spectrum Sensing Method based on Convolutional Neural Network,” Systems Engineering and Electronics Technology, Vol. 41, No. 1, pp. 178-186, 2019
13.	D. H.Pham and S. Meignen, “High-Order Synchrosqueezing Transform for Multicomponent Signals Analysis—With an Application to Gravitational-Wave Signal,” IEEE Transactions on Signal Processing, Vol. 65, No. 12, pp. 3168-3178, 2017
14.	R. G. Stockwell, L. Mansinha,R. P. Lowe, “Localization of the Complex Spectrum: The S Transform,” IEEE Transactions on Signal Processing, Vol. 44, No. 4, pp. 998-1001, 2002
15.	Y. LeCun, K. Kavukcuoglu,C. Farabet, “Convolutional Networks and Applications in Vision,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems, IEEE, 2010

[1]	Pancham Singh, Updesh Kumar Jaiswal, Eshank Jain, Nikhil Kumar, and Vimlesh Mishra. A Novel Methodology Utilizing Modern CCTV Cameras and Software as a Service Model for Crime Detection and Prediction [J]. Int J Performability Eng, 2025, 21(2): 112-121.
[2]	Seema Kalonia and Amrita Upadhyay. Comparative Analysis of Machine Learning Model and PSO Optimized CNN-RNN for Software Fault Prediction [J]. Int J Performability Eng, 2025, 21(1): 48-55.
[3]	Manu Banga. Enhancing Software Fault Prediction using Machine Learning [J]. Int J Performability Eng, 2024, 20(9): 529-540.
[4]	Aditya Dayal Tyagi, and Krishna Asawa. Influence Maximization in Social Network using Community Detection and Node Modularity [J]. Int J Performability Eng, 2024, 20(9): 552-562.
[5]	Ekta Singh, and Parma Nand. Efficient Multi-Class Facial Emotion Recognition using YOLOv9: A Deep Learning Approach for Real-Time Applications [J]. Int J Performability Eng, 2024, 20(9): 581-590.
[6]	Manpreet Singh, Gauri Jindal, Akshita Oberoi, and Rohan Dhangar. Improving Crime Detection Through Geo-MDA: A Hybrid Linear Regression Approach in Data Mining [J]. Int J Performability Eng, 2024, 20(8): 469-477.
[7]	Nilesh Shelke, Deepali Sale, Sagar Shinde, Atul Kathole, and Rachna Somkunwar. A Comprehensive Framework for Facial Emotion Detection using Deep Learning [J]. Int J Performability Eng, 2024, 20(8): 487-497.
[8]	Lakshya Vaswani, Sai Sri Harsha, Subham Jaiswal, and Aju D. Unravelling Complexity: Investigating the Effectiveness of SHAP Algorithm for Improving Explainability in Network Intrusion System Across Machine and Deep Learning Models [J]. Int J Performability Eng, 2024, 20(7): 421-431.
[9]	Rohit Kumar Verma and Sukhvir Singh. A Hybrid Framework of Resource Allocation using Firefly and Deep Learning in Big Data Scheduling [J]. Int J Performability Eng, 2024, 20(6): 333-343.
[10]	Mukta Jagdish and Valliappan Raju. Multimodal Sign Language Recognition System: Integrating Image Processing and Deep Learning for Enhanced Communication Accessibility [J]. Int J Performability Eng, 2024, 20(5): 271-281.
[11]	Priya Singh and Rajalakshmi Krishnamurthi. AgriGuard: IoT-Powered Real-Time Object Detection and Alert System for Intelligent Surveillance [J]. Int J Performability Eng, 2024, 20(4): 232-241.
[12]	Ujjwal Deep, Sushant Kumar, and Kanika Singla. Integrating Deep Learning Architectures for Enhanced Human Action Recognition: An Ensemble Approach [J]. Int J Performability Eng, 2024, 20(4): 253-262.
[13]	Atul Kumar and Gurpreet Singh Lehal. Layout Detection of Punjabi Newspapers using the YOLOv8 Model [J]. Int J Performability Eng, 2024, 20(3): 186-193.
[14]	Shou-Yu Lee, Yu-Sheng Chu, Tzu-Wei Hsu, I-Hsiang Yu, and W. Eric Wong. Enhanced Recognition Approach for Herb Medicine using YOLOv8 in Medical Information Systems [J]. Int J Performability Eng, 2024, 20(12): 713-722.
[15]	Meenakshi Chawla and Meenakshi Pareek. Hybridizing Intelligence: A Comparative Study of Machine Learning Algorithm and ANN-PSO Deep Learning Model for Software Effort Estimation [J]. Int J Performability Eng, 2024, 20(11): 668-675.

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

PDF

Knowledge

Abstract

Cite this article

share this article

References 15.

Related Articles 15

Recommended 0