@article{Chen2024, 
author = {Chen Chen and Zhongwei Xu and Meng Mei and Kai Huang and Siu Ming Lo},
title = {Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine},
year = {2024},
journal = {Computers, Materials & Continua},
volume = {79},
number = {3},
pages = {4533-4549},
keywords = {Railway switch machine, tensor machine, fault diagnosis},
url = {https://www.sciopen.com/article/10.32604/cmc.2024.048995},
doi = {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.}
}