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基于T-S故障树的液压轮边制动系统可靠性分析
引用本文:姚成玉,侯安农,陈东宁,潘昊洋,吕世君.基于T-S故障树的液压轮边制动系统可靠性分析[J].液压与气动,2019,0(6):11-16.
作者姓名:姚成玉  侯安农  陈东宁  潘昊洋  吕世君
作者单位:1. 燕山大学河北省工业计算机控制工程重点实验室, 河北秦皇岛066004; 2. 燕山大学河北省重型机械流体动力传输与控制重点实验室, 河北秦皇岛066004; 3. 先进锻压成形技术与科学教育部重点实验室(燕山大学), 河北秦皇岛066004
基金项目:国家自然科学基金(51675460,51405426);中国博士后科学基金(2017M621101);河北省自然科学基金(E2016 203306)
摘    要:通过分析液压轮边制动系统原理,以故障模式为底事件建造液压轮边制动系统T-S故障树。针对实际系统故障数据缺乏、故障机理复杂多样等原因导致的T-S故障树底事件失效可能性的不确定性问题,将区间模型引入T-S故障树分析,解决底事件失效可能性不易精确获取的问题;进而对系统进行可靠性分析,求得液压轮边制动系统故障树顶事件的故障概率、各底事件的概率重要度和关键重要度,为系统维护和故障发现提供依据。

关 键 词:液压轮边制动系统  可靠性分析  T-S故障树  区间模型  重要度
收稿时间:2018-08-31

Reliability Analysis Based on T-S Fault Tree for Hydraulic Wheel Braking System
YAO Cheng-yu,HOU An-nong,CHEN Dong-ning,PAN Hao-yang,LV Shi-jun.Reliability Analysis Based on T-S Fault Tree for Hydraulic Wheel Braking System[J].Chinese Hydraulics & Pneumatics,2019,0(6):11-16.
Authors:YAO Cheng-yu  HOU An-nong  CHEN Dong-ning  PAN Hao-yang  LV Shi-jun
Affiliation:1. Key Laboratory of Industrial Computer Control Engineering of Hebei Province,Yanshan University, Qinhuangdao, Hebei066004; 2. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control,Yanshan University, Qinhuangdao, Hebei066004; 3. Key Laboratory of Advanced Forging & Stamping Technology and Science(Yanshan University),Ministry of Education of China, Qinhuangdao, Hebei066004
Abstract:By analyzing principle of hydraulic wheel braking system, we establish a T-S fault tree of hydraulic wheel braking system based on fault mode. In order to solve the uncertainty of basic events failure possibilities in actual system due to poor fault data and complex fault mechanisms, an interval model is introduced into the T-S fault tree analysis. The failure probability of the top event and the probability importance degree and critical importance degree of each basic event can be obtained by analysis. It provides a theoretical basis for system maintenance and fault finding.
Keywords:hydraulic wheel braking system  reliability analysis  T-S fault tree  interval model  importance degree
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