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Sensor Configuration and Test for Fault Diagnoses of Subway Braking System Based on Signed Digraph Method

     

摘要

Fault diagnosis of various systems on rolling stock has drawn the attention of many researchers.However,obtaining an optimized sensor set of these systems,which is a prerequisite for fault diagnosis,remains a major challenge.Available literature suggests that the configuration of sensors in these systems is presently dependent on the knowledge and engineering experiences of designers,which may lead to insufficient or redundant development of various sensors.In this paper,the optimization of sensor sets is addressed by using the signed digraph(SDG)method.The method is modified for use in braking systems by the introduction of an effect-function method to replace the traditional quantitative methods.Two criteria are adopted to evaluate the capability of the sensor sets,namely,observability and resolution.The sensors configuration method of braking system is proposed.It consists of generating bipartite graphs from SDG models and then solving the set cover problem using a greedy algorithm.To demonstrate the improvement,the sensor configuration of the HP2008 braking system is investigated and fault diagnosis on a test bench is performed.The test results show that SDG algorithm can improve single-fault resolution from 6 faults to 10 faults,and with additional four brake cylinder pressure(BCP)sensors it can cover up to 67 double faults which were not considered by traditional fault diagnosis system.SDG methods are suitable for reducing redundant sensors and that the sensor sets thereby obtained are capable of detecting typical faults,such as the failure of a release valve.This study investigates the formal extension of the SDG method to the sensor configuration of braking system,as well as the adaptation supported by the effect-function method.

著录项

  • 来源
    《中国机械工程学报》|2014年第3期|475-482|共8页
  • 作者

    ZUO Jianyong; CHEN Zhongkai;

  • 作者单位

    Institute of Railway and Urban Mass Transit, Tongji University, Shanghai 201804, China;

    Institute of Railway and Urban Mass Transit, Tongji University, Shanghai 201804, China;

  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2023-07-25 20:48:56

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