Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance. (January 2019)

• Record Type: Journal Article
• Authors: Zhang, Xin; Wang, Jiaxu; Liu, Zhiwen; Wang, Jinglin
• Is Part Of: ISA transactions. Volume 84(2019)
• Page Start: 283
• Page End: 295
• Publication Date: 2019-01
• Subjects: Fault diagnosis -- Vibration signal processing -- Weak feature enhancement -- Empirical wavelet transform -- Improved adaptive bistable stochastic resonance -- Salp swarm algorithm; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2018.09.022
• Languages: English
• ISSNs: 0019-0578

Abstract

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. Highlights: A fault feature enhancement method based on EWT and an IABSR is proposed. EWT is used as the preprocessing program to decompose the signal into a set of sub-components. The IABSR method is developed to enhance fault feature information. Numerical simulation validates the effectiveness and advantages of the IABSR method. Case studies on machinery fault diagnosis demonstrate superiority of the proposed method.