Application of multi-scale noise tuning parameter-induced stochastic resonance for planetary gearbox diagnosis

ABSTRACT

Multi-scale noise tuning parameter-induced stochastic resonance method (MNTPSR-1/f ^β) is proposed for planetary gearbox diagnosis. This method is divided into two steps. The first step is called multi-scale noise tuning (MNT) and the second step called parameter-induced stochastic resonance (PSR). MNT based on wavelet packet transformation tunes the noise to an approximate 1/f ^β noise which can improve PSR effect. PSR enhances the weak high-frequency fault characteristic signal. Some simulations are designed for analysing MNTPSR-1/f ^β performance such as the MNT effect, anti-noise capability, and optimal frequency response. A fault diagnosis strategy for the planetary gearbox is proposed. To validate the proposed method, two pre-planting fault tests of sun gear and ring gear are carried out. For comparison, PSR without MNT, MNTPSR-1/f and MNTPSR-1/f ^β are used to analyse the fault signals. The simulations and case validations prove that our proposed method is the most effective.

Abbreviations: wavelet packet transformation (WPT), stochastic resonance (SR)

KEYWORDS:

• Fault diagnosis
• planetary gearbox
• multi-scale noise tuning
• wavelet packet transformation
• stochastic resonance

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71701038];

Notes on contributors

Kuo Chi

Kuo Chi received the B.Sc. degree in mechanical engineering from Jimei University, Xiamen, China, in 2013 and the M.S. degree in Maintenance Engineering from Mechanical Engineering College, Shijiazhuang, China, in 2015. Now, he is a full-time Ph.D. student of the Army Engineering University of PLA, Shijiazhuang, China. His current research is focused on mechanical fault detection, diagnostics, and prognostics.

Jianshe Kang

Jianshe Kang is received the Ph.D. degree in Mechatronical Engineering from Beijing Institute of Technology, Beijing, China. He is a professor at Army Engineering University of PLA, Shijiazhuang, China. He is a direct general of China Ordnance Industry Society and selected as the editorial board of Acta Armamentarill. He authored one book in the field of maintenance engineering. He published about 60 journal papers. His current research interests include system reliability analysis, condition based prognostics and health management of capital assets.

Xinghui Zhang

Xinghui Zhang received the Ph.D. degree in Mechanical Engineering College of Shijiazhuang, China, in 2015. Now he is a postdoctoral of Mechanical Engineering College, Shijiazhuang, China. He has published about 30 journal papers in the fields of reliability engineering and mechanical engineering. His current research interests include mechanical fault diagnosis, fault prognosis, performance-based contracts and digital signal processing.

Zhao Fei

Zhao Fei received her Ph.D. degree in Donlinks School of Economics and Management, University of Science and Technology Beijing (USTB). She is currently an assistant professor in School of Business Administration, Northeast University. Her main research interests include system maintenance, reliability, and the spare parts management. She has published more than 10 papers in journals, such as Reliability Engineering & System Safety, Journal of the Operational Research Society and Part O: Journal of Risk and Reliability.