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