Adaptive fault feature extraction from wayside acoustic signals from train bearings. (7th July 2018)

• Record Type: Journal Article
• Authors: Zhang, Dingcheng; Entezami, Mani; Stewart, Edward; Roberts, Clive; Yu, Dejie
• Is Part Of: Journal of sound and vibration. Volume 425(2018)
• Page Start: 221
• Page End: 238
• Publication Date: 2018-07-07
• Subjects: Wayside acoustic detection -- Train bearing -- Improved singular value decomposition -- Savitzky-Golay smoothing filter -- Resonance-based Signal Sparse Decomposition; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2018.04.004
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods. Highlights: A novel ISVD-RSSD method is proposed for wayside acoustic detection of train bearing. A new wayside acoustic monitoring system is built to detect train bearing faults. ESE and SOS are introduced to adaptively select parameters of the S-G filter. Harmonic components can be excluded from wayside acoustic signal by using RSSD. Laboratory and field experiments verify the effectiveness of the proposed method.