An Improved VMD Method for Use with Acoustic Impact Response Signals to Detect Corrosion at the Underside of Railway Tracks

Jingyuan Yang, Edward Stewart, Jiaqi Ye, Mani Entezami, Clive Roberts

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Abstract

Variational Mode Decomposition (VMD) is widely used for inspection purposes. The initial parameters are usually set manually, which is a limitation of this technique. In this paper, a method to automatically select these parameters through a combination of Singular Value Decomposition (SVD) and Improved-VMD (IVMD) is proposed. VMD is applied multiple times with a varying K-value parameter. The original signal and its sub-signals arising from VMD decomposition are all subjected to SVD. An index representing the relevance between sub-signals and the original signal is obtained by comparing eigenvalues, which are calculated by SVD. The result shows the effectiveness of VMD with different initial K-value parameters. SVD is then further applied to the VMD result for the selected K-value parameter to obtain Shannon entropy, which can be used in the detection and classification of corrosion on the underside of the rail. Comparing with current energy-based methods, the Shannon entropy obtained by IVMD–SVD has the advantage of reducing environmental interference to obtain more uniform energy results. The proposed method can improve the effectiveness of VMD for the impact response signal. The classification of underside corrosion of rails can be realised according to the results obtained from the proposed method.
Original languageEnglish
Article number942
Number of pages15
JournalApplied Sciences
Volume13
Issue number2
DOIs
Publication statusPublished - 10 Jan 2023

Keywords

  • impact response signal
  • VMD
  • SVD
  • rail
  • corrosion inspection
  • machine learning
  • Article

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