A Platform for Fault Diagnosis of High-Speed Train based on Big Data

Quan XU, Peng ZHANG, Wenqin LIU, Qiang LIU, Changxin LIU, Liangyong WANG, Anthony TOPRAC, S.Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

11 Citations (Scopus)


High-speed trains are very fast (e.g. 350km/h) and operate at high traffic density, so once a fault has occurred, the consequences are disastrous. In order to better control the train operational status by timely and rapid detection of faults, we need new methods to handle and analyze the huge volumes of high-speed railway data. In this paper, we propose a novel framework and platform for high-speed train fault diagnosis based on big data technologies. The framework aims to allow researchers to focus on fault detection algorithm development and on-line application, with all the complexities of big data import, storage, management, and realtime use handled transparently by the framework. The framework uses a combination of cloud computing and edge computing and a two-level architecture that handles the massive data of train operations. The platform uses Hadoop as its basic framework and combines HDFS, HBase, Redis and MySQL database as the data storage framework. A lossless data compression method is presented to reduce the data storage space and improve data storage efficiency. In order to support various types of data analysis tasks for fault diagnosis and prognosis, the framework integrates online computation, off-line computation, stream computation, real-time computation and so on. Moreover, the platform provides fault diagnosis and prognosis as services to users and a simple case study is given to further illustrate how the basic functions of the platform are implemented.
Original languageEnglish
Pages (from-to)309-314
Number of pages6
Issue number18
Early online date8 Oct 2018
Publication statusPublished - 2018
Externally publishedYes


  • Big Data
  • Cloud Computing
  • Edge Computing
  • Fault Diagnosis
  • High-Speed Train


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