Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance

Xin Zhang; Jiaxu Wang; Zhiwen Liu; Jinglin Wang

doi:10.1016/j.isatra.2018.09.022

Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance

ISA Trans. 2019 Jan:84:283-295. doi: 10.1016/j.isatra.2018.09.022. Epub 2018 Oct 1.

Authors

Xin Zhang¹, Jiaxu Wang², Zhiwen Liu³, Jinglin Wang⁴

Affiliations

¹ School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan 610065, PR China. Electronic address: zhangx_15@126.com.
² School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan 610065, PR China.
³ School of Aeronautics and Astronautics, Sichuan University, Chengdu, Sichuan 610065, PR China. Electronic address: lzw1682007@126.com.
⁴ Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management, AVIC Shanghai Aeronautical Measurement-Controlling Research Institute, Shanghai 201601, PR China.

PMID: 30309725
DOI: 10.1016/j.isatra.2018.09.022

Abstract

Machinery vibration signal is a typical multi-component signal and fault features are often submerged by some interference components. To accurately extract fault features, a weak feature enhancement method based on empirical wavelet transform (EWT) and an improved adaptive bistable stochastic resonance (IABSR) is proposed. This method makes full use of the signal decomposition performance of EWT and the signal enhancement of the IABSR to achieve the purpose of fault feature enhancement in low frequency band of FFT spectrum. Firstly, EWT is used as the preprocessing program of bistable stochastic resonance (BSR) to decompose the machinery vibration signal into a set of sub-components. Then, the sensitive component that contains main fault information is further input into BSR system to enhance fault features with the assistance of residual noises. Finally, the fault features are identified from fast Fourier transform (FFT) spectrum of the BSR output. To achieve the optimal BSR output, the IABSR method based on salp swarm algorithm (SSA) is presented. Compared with the tradition adaptive BSR (ABSR), the IABSR optimizes not only the BSR system parameters but also the calculation step size. Two case studies on machinery fault diagnosis demonstrate the effectiveness and superiority of the proposed method. In addition, the proposed method is easy to implement and is robust to noise to some extent.

Keywords: Empirical wavelet transform; Fault diagnosis; Improved adaptive bistable stochastic resonance; Salp swarm algorithm; Vibration signal processing; Weak feature enhancement.