Map-Reduce Decentralized PCA for Big Data Monitoring and Diagnosis of Faults in High-Speed Train Bearings

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

12 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)144-149
Journal / PublicationIFAC-PapersOnLine
Volume51
Issue number18
Online published8 Oct 2018
Publication statusPublished - 2018
Externally publishedYes

Abstract

Real-time fault detection and diagnosis of high speed trains is essential for the operation safety. Traditional methods mainly employ rule-based alarms to detect faults when the measured single variable deviates too far from the expected range, with multivariate data correlations ignored. In this paper, a Map-Reduce decentralized PCA algorithm and its dynamic extension are proposed to deal with the large amount of data collected from high speed trains. In addition, the Map-Reduce algorithm is implemented in a Hadoop-based big data platform. The experimental results using real high-speed train operation data demonstrate the advantages and effectiveness of the proposed methods for five faulty cases.

Research Area(s)

  • Big Data Modeling, Decentralized Principal Component Analysis, Fault Diagnosis, High-Speed Train Operation Safety