Title: Monitoring data-based automatic fault diagnosis for the brake pipe of high-speed train

Authors: Guo Xie; Minying Ye; Xinhong Hei; Fucai Qian

Addresses: Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China ' Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China ' Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China ' Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China

Abstract: As a key part of a railway system, the brake pipe is essential for brake system, and its fault may cause serious consequences, and even threaten lives. The generally employed approach for the fault diagnosis of brake pipe is manual inspection during the parking time, which is time-consuming, laborious, and immensely dependent on the self-experience of the inspectors. In view of these problems, an automatic fault diagnosis analysis for the brake pipe of high-speed train based on monitoring data is proposed in this paper. Based on the concept of big data, the characteristics of monitoring data are analysed, and the fault features are extracted, then the fault diagnosis rules are established. Specifically, the main steps are as follows: first step is the data pre-processing, including correcting the singular zero points and populating the missing points; second step aims to eliminate the noise and measurement errors; third step includes the establishment of the fault diagnosis rules, data analysis and fault diagnosis. Lastly, the data from an actual train line is analysed, and the analysis results demonstrate the effectiveness and feasibility of the proposed method.

Keywords: high-speed train; fault diagnosis; brake pipe pressure; data-based.

DOI: 10.1504/IJCAT.2018.092977

International Journal of Computer Applications in Technology, 2018 Vol.57 No.3, pp.246 - 254

Received: 28 Aug 2016
Accepted: 08 Mar 2017
Published online: 04 Jul 2018 *

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