A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems

Fault diagnosis is a key way to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a fault diagnosis method based on belief rule base with mixed reliability (BRB-mr). Different from the traditional BRB, this method considers two kinds of interference factors that affect the observation data in engineering practice, including the performance of sensors and the influence of external environment, and we quantify them as static reliability and dynamic reliability of attributes in BRB. In order to integrate two kinds of reliability factors into the reasoning of BRB, a discount method is developed based on Dempster-Shafer theory (D-S theory), which is helpful for more accurate diagnosis. In this paper, the effectiveness and practicability of the method are verified by a single fault of the running gear, and the supplementary numerical data verified its feasibility in multiple fault mode diagnosis. Then this method is compared with traditional methods. The result shows BRB-mr model has stronger diagnostic ability to identify faults and it has a certain engineering application value to be extended to other complex system fault diagnosis.

I. Introduction

The emergence of large-scale engineering and macro engineering promotes the development of large complex electromechanical systems [1]. As a typical representative of this kind of systems, high-speed trains have been one of the most important and progressive branches of intelligent transportation [2]. Their features are the vast majority of on-orbit time which is in high-speed operation [3]. This is closely related to the safety of passengers’ personal safety and property security, so its requirements on system of reliability and safety are very stringen [4]. In the field of intelligent transportation and academia [7], [10], [11], fault diagnosis is a widely accepted solution by scholars, which is mainly used to judge and manage faults [5].