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Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

Chen Chen1,2, Zhongwei Xu1, Meng Mei1,*, Kai Huang3, Siu Ming Lo2

1 School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China
2 Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China
3 School of Computer Engineering, Jimei University, Xiamen, 361021, China

* Corresponding Author: Meng Mei. Email: email

(This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)

Computers, Materials & Continua 2024, 79(3), 4533-4549. https://doi.org/10.32604/cmc.2024.048995

Abstract

Railway switch machine is essential for maintaining the safety and punctuality of train operations. A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein. Unlike existing methods, this approach takes into account the spatial information of the time series monitoring data, aligning with the domain expertise of on-site manual monitoring. Besides, a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient information. First, one-dimensional signal data is preprocessed and transformed into two-dimensional images. Afterward, the fusion feature tensor is created by utilizing the images of the three-phase current and employing the CANDECOMP/PARAFAC (CP) decomposition method. Then, the tensor learning-based model is built using the extracted fusion feature tensor. The developed fault diagnosis scheme is valid with the field three-phase current dataset. The experiment indicates an enhanced performance of the developed fault diagnosis scheme over the current approach, particularly in terms of recall, precision, and F1-score.

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APA Style
Chen, C., Xu, Z., Mei, M., Huang, K., Lo, S.M. (2024). Fault diagnosis scheme for railway switch machine using multi-sensor fusion tensor machine. Computers, Materials & Continua, 79(3), 4533-4549. https://doi.org/10.32604/cmc.2024.048995
Vancouver Style
Chen C, Xu Z, Mei M, Huang K, Lo SM. Fault diagnosis scheme for railway switch machine using multi-sensor fusion tensor machine. Comput Mater Contin. 2024;79(3):4533-4549 https://doi.org/10.32604/cmc.2024.048995
IEEE Style
C. Chen, Z. Xu, M. Mei, K. Huang, and S.M. Lo "Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine," Comput. Mater. Contin., vol. 79, no. 3, pp. 4533-4549. 2024. https://doi.org/10.32604/cmc.2024.048995



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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