Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (9): 331-337.doi: 10.11896/jsjkx.231200190

• Computer Network • Previous Articles     Next Articles

Edge Cloud Computing Approach for Intelligent Fault Detection in Rail Transit

LI Zhi1,2, LIN Sen1, ZHANG Qiang3   

  1. 1 School of Automation and Intelligence,Beijing Jiaotong University,Beijing 100044,China
    2 The 15th Research Institute of China Electronics Technology Group Corporation,Beijing 100083,China
    3 The Chinese People's Liberation Army 61741 Unit,Beijing 100070,China
  • Received:2023-12-27 Revised:2024-05-24 Online:2024-09-15 Published:2024-09-10
  • About author:LIN Sen,born in 1999,postgraduate.His main research interests include urban rail transit and artificial intelligence.

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Abstract: Rail transit systems are the main carrying system of transportation capacity in the current society.It is extremely sensitive to safety.Because multiple components of the system are directly exposed to the environment,they are affected by various environments and are prone to failures,which may cause train delays,passenger retention,service outage,or even catastrophic loss of life or property.Therefore,it is necessary to design a fault detection scheme so that effective maintenance measures can be taken.Different from traditional machine learning(ML) based fault classification work,this paper adopts Chinese bidirectional encoder representation from transformer(BERT) deep learning(DL) model for intelligent fault detection.The model can obtain bidirectional contextual understanding when dealing with fault detection tasks,so as to more accurately capture the semantic relationship in sentences,and understand the fault descriptions more accurately.The training of BERT requires a large amount of data support,and there are multiple operators in the field of rail transit,each of which holds independent fault detection data.Due to the confidentiality of the data,these data cannot be shared,which limits the training of the BERT model.This paper designs and adopts the federated edge cloud computing method,allowing multiple operators to jointly train the BERT model while maintaining data privacy.Federated learning combined with the edge cloud computing method allows the data of rail transit operators to be preliminary processed locally,and then the summarized gradients are uploaded to the cloud for model training,and finally the trained model parameters are sent back to each edge device to realize model updates.The research results show that the BERT model training using the federated edge cloud computing method is superior to the existing advanced solutions in the fault detection task in the field of rail transit.This method not only solves the problem of data confidentiality,but also effectively improves the accuracy and reliability of fault detection.

Key words: Rail transit, Fault detection, Edge cloud computing, Federated learning, Bidirectional encoder representation from transformer

CLC Number: 

  • U285.4
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