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| 受电弓系统可靠性评估的超椭球贝叶斯网络方法 | |
| 引用本文： | 齐金平,周亚辉,王康,李少雄. 受电弓系统可靠性评估的超椭球贝叶斯网络方法[J]. 兰州理工大学学报, 2022, 48(4): 105 |
| 作者姓名： | 齐金平 周亚辉 王康 李少雄 |
| 作者单位： | 1.兰州交通大学 机电技术研究所, 甘肃 兰州 730070; 2.甘肃省物流及运输装备信息化工程技术研究中心, 甘肃 兰州 730070; 3.甘肃省物流与运输装备行业技术中心, 甘肃 兰州 730070 |
| 基金项目： | 国家自然科学基金(71861021),甘肃省重点研发项目(17YF1FA122),甘肃省高等学校科研项目(2018A-026,2018C-10),铁路总公司科研计划课题(2015T002-D) |
| 摘 要： | 针对受电弓系统复杂多态特性以及故障概率难以精确表达的问题,且现有研究主要滞留于结合证据理论的模糊贝叶斯网络的现状,首次将超椭球贝叶斯网络引入受电弓可靠性分析中,规避了模糊贝叶斯网络区间取极值的情况,使得根节点的概率取值区间进一步被界定;进而以超椭球贝叶斯网络求解受电弓系统在不同故障状态下的叶节点故障率、灵敏度、后验概率等可靠性参数,找出了影响系统可靠性的高风险事件.经与模糊贝叶斯网络对比可知,超椭球贝叶斯网络区间更小,验证了新方法的正确性与实用性. |
| 关 键 词： | 受电弓 多态性 超椭球贝叶斯网络 灵敏度 后验概率 |
| 收稿时间： | 2021-03-17 |
Study on hyper-ellipsoidal Bayesian network method for evaluating reliability of pantograph system |
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| QI Jin-ping,ZHOU Ya-hui,WANG Kang,LI Shao-xiong. Study on hyper-ellipsoidal Bayesian network method for evaluating reliability of pantograph system[J]. Journal of Lanzhou University of Technology, 2022, 48(4): 105 | |
| Authors: | QI Jin-ping ZHOU Ya-hui WANG Kang LI Shao-xiong |
| Affiliation: | 1. Mechatronics T&R Institute, Lanzhou Jiaotong University, Lanzhou 730070, China; 2. Gansu Provincial Engineering Technology Center for Informatization of Logistics & Transport Equipment, Lanzhou 730070, China; 3. Gansu Provincial Industry Technology Center of Logistics & Transport Equipment, Lanzhou 730070, China |
| Abstract: | Aiming at the characteristic of complex polymorphism of the pantograph system and the difficulty in expressing the accuracy of the failure probability, and the current situation that the existing research is mainly stuck in the fuzzy Bayesian network combined with the evidence theory, the hyper-ellipsoidal Bayesian network is introduced for the first time into the reliability analysis of pantograph, which avoids the extreme value of the fuzzy Bayesian network interval and further defines the probability value interval of the root node. Then the hyper-ellipsoidal Bayesian network is used to solve the reliability parameters such as leaf node failure rate, sensitivity and posterior probability of the pantograph system under different fault states, and the high-risk events of the system are found out. Compared with the fuzzy Bayesian network, it can be seen that the interval of the hyper-ellipsoidal Bayesian network is smaller, which verifies the correctness and practicability of the new method. |
| Keywords: | pantograph multi-state hyper-ellipsoidal Bayesian networks sensitivity posteriori probability |
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