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Open AccessArticle

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System

1
School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
2
CRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062, China
3
Department of Automation, Tsinghua University, Beijing 100084, China
4
Academy of Astronautics, Harbin Institute of Technology, Harbin 150001, China
5
School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1017; https://doi.org/10.3390/s20041017
Received: 5 January 2020 / Revised: 31 January 2020 / Accepted: 11 February 2020 / Published: 13 February 2020
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods. View Full-Text
Keywords: fault diagnosis; distributed state estimation; state degradation fault diagnosis; distributed state estimation; state degradation
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MDPI and ACS Style

Cheng, C.; Wang, W.; Luo, H.; Zhang, B.; Cheng, G.; Teng, W. State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System. Sensors 2020, 20, 1017.

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