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Multiple damage detection of maglev rail joints using time-frequency spectrogram and convolutional neural network

  • Wang, Su-Mei (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Jiang, Gao-Feng (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Ni, Yi-Qing (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Lu, Yang (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Lin, Guo-Bin (Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University) ;
  • Pan, Hong-Liang (Maglev Transportation Engineering R&D Center, Tongji University) ;
  • Xu, Jun-Qi (Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University) ;
  • Hao, Shuo (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • Received : 2021.06.27
  • Accepted : 2022.01.03
  • Published : 2022.04.25
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Abstract

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

Keywords

Acknowledgement

The research described in this paper was supported by a grant (RIF) from the Research Grants Council of the Hong Kong Special Administrative Region (SAR), China (Grant No. R-5020-18), a grant from the National Natural Science Foundation of China (Grant No. U1934209) and Wuyi University's Hong Kong and Macao Joint Research and Development Fund (Grants No. 2019WGALH15 and 2019WGALH17). The authors would also like to appreciate the funding support by the Innovation and Technology Commission of the Hong Kong SAR Government to the Hong Kong Branch of Chinese National Rail Transit Electrification and Automation Engineering Technology Research Center (Grant No. K-BBY1) and by the National Natural Science Foundation of China to the Maglev Transportation Engineering R&D Center (Grant No. 52072269).

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