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Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation

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Abstract

In this paper, the vibration-based image representation and data fusion demonstrates distinctive benefit in feature extraction, yielding superior performance for damage identification in railway engineering. Specifically, based on vehicle-track coupled dynamics, the rail vibration datasets under diverse fastener damage conditions are generated. By converting 1-D vibration signals into 2-D grayscale images with recurrence plots (RPs) and the aid of conditional variational autoencoder (CVAE), the acceleration RPs and displacement RPs are fused for enhancing feature extraction. It is demonstrated that detecting the variation in texture patterns and color distribution of the vibration-based images facilitates effective damage identification, mitigating the sensitivity of damage recognition to the deterioration of track irregularity. The results show that the displacement RPs characterised by quasi-static features are more suitable for fastener damage identification. Further, by employing the data fusion that combines both the random dynamic features of the acceleration RPs and quasi-static features of the displacement RPs, the tolerance of measurement range for accurate fastener damage identification can be extended. The robustness of the proposed method is validated after testing different sampling frequencies and additional noise.

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Acknowledgements

The authors acknowledge continuous supports by the China Scholarship Council and the Royal Society of New Zealand.

Funding

This work was supported by the Ph.D. scholarship from the China Scholarship Council (Grant No. 202007000010), Catalyst Seeding General Grant administered by the Royal Society of New Zealand (Contract 20-UOA-035-CSG).

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Authors and Affiliations

  1. Department of Mechanical and Mechatronics Engineering, The University of Auckland, 5 Grafton Road, Auckland, 1010, New Zealand

    Shaohua Wang, Lihua Tang & Kean C. Aw

  2. Chengdu Vocational & Technical College of Industry, Chengdu, 610031, China

    Yinling Dou

  3. Institute of Science and Technology, China Railway Eryuan Engineering Group Co., Ltd., Chengdu, 610031, China

    Zhaoyu Li

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  1. Shaohua Wang
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Contributions

Conceptualization, Methodology and Writing—original draft were performed by SW. Supervision, Writing—review & editing and Funding acquisition were performed by LT. Software and Investigation were performed by YD. Writing—review & editing was performed ZL. Supervision was performed by KCA.

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Correspondence to Lihua Tang.

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Wang, S., Tang, L., Dou, Y. et al. Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation. J Nondestruct Eval 43, 15 (2024). https://doi.org/10.1007/s10921-023-01028-7

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