An Adaptive Graph Morlet Wavelet Transform for Railway Wayside Acoustic Detection

Dingcheng Zhang*, Min Xie, Moussa Hamadache, Mani Entezami, Edward Stewart

*Corresponding author for this work

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    24 Citations (Scopus)

    Abstract

    Monitoring condition of train axle bearings is significant for ensuring the safety of train operation. Wayside acoustic detection technology is a promising tool for early fault detection of train axle bearings. However, the strong background noise exists in collected acoustic signals, which normally masks the fault feature of axle bearings. In this work, a novel method called adaptive graph Morlet wavelet transform (AGMWT), is proposed for fault feature extraction in railway wayside acoustic detection. In AGMWT, acoustic signals are firstly transformed into horizontal visibility graphs. Next, the inner product operation is conducted to measure the similarity between the graph and daughter wavelets which are translated and scaled versions of the mother Morlet wavelet. An adaptive wavelet threshold and a shrinkage strategy are then proposed to shrink the graph Morlet wavelet coefficient, and finally the denoised signal can be obtained using inverse transform. To improve denoising performance, parameters of the mother Morlet wavelet are then optimised according to the Hilbert envelope spectrum fault feature ratio and the Hilbert envelope entropy. The effectiveness of the proposed method has been verified by conducting simulation, laboratory and field experiments. In addition, the denoising ability of AGMWT has advantages comparing other methods.
    Original languageEnglish
    Article number116965
    JournalJournal of Sound and Vibration
    Volume529
    Online published17 Apr 2022
    DOIs
    Publication statusPublished - 7 Jul 2022

    Funding

    This study was supported by the Fundamental Research Funds for the Central Universities, a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. T32-101/15-R), the National Key Research and Development Program of China (No. 2021YFB3300801) and National Nature Science Foundation of China (Project No. 52105115, 62120106011), also makes use of data recorded during previous studies sponsored by Hitachi Rail Europe. The authors would like to express their gratitude to Rail Alliance and Motorail Logistics for providing access to their test facilities.

    Research Keywords

    • Graph wavelet transform
    • Railway
    • Spectral graph theory
    • Train axle bearing
    • Wayside acoustic detection

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