基于置信规则库专家系统的司控器开关量健康状态评估
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摘要
微动开关是轨道车辆司控器常用的开关设备,对其健康状态评估是保证轨道车辆运行安全的前提.针对司控器微动开关数据样本少、诊断信号具有波动性和非线性、健康状态评估困难等问题,提出一种基于置信规则库专家系统(BRB)的司控器开关量健康状态评估方法.首先,分析微动开关失效机理与故障特征的关系;然后,采用置信规则库将定性知识与定量信息有效结合,采用证据推理(ER)算法进行知识推理,并对所建立的模型初始参数进行优化,得到最优的参数集合,从而提高轨道车辆微动开关健康状态评估的准确性.通过对模型训练及测试,所得结果表明,所提出的方法能准确地评估微动开关状态,便于早期发现故障、跟踪故障发展趋势和及时更换失效部件.
The microswitch is a common switchgear of the railway vehicle driver controller, and the evaluation of its health condition is the prerequisite to ensure the safety of railway vehicle operation. Aiming at the problem that the data of the controller microswitch is few, the signal is nonlinear, and the health status is difficult to assess, a method for evaluating the health status of microswitch based on the belief rule base(BRB) is proposed. Firstly, the relation between the failure mechanism and the fault characteristics of the microswitch is analyzed, and the qualitative knowledge and quantitative information are effectively combined with the BRB, the knowledge is reasoned by using the evidential reasoning(ER) algorithm, the initial parameters of the model are optimized, and the optimal set of parameters is obtained,which improves the accuracy of evaluating the health status of the microswitch. Through the training and testing of the model, the experimental results show that the method can accurately evaluate the fault of the microswitch in railway vehicle, which is convenient to detect faults early, track the development trend of failure and replace the failed components in a timely manner.
The microswitch is a common switchgear of the railway vehicle driver controller, and the evaluation of its health condition is the prerequisite to ensure the safety of railway vehicle operation. Aiming at the problem that the data of the controller microswitch is few, the signal is nonlinear, and the health status is difficult to assess, a method for evaluating the health status of microswitch based on the belief rule base(BRB) is proposed. Firstly, the relation between the failure mechanism and the fault characteristics of the microswitch is analyzed, and the qualitative knowledge and quantitative information are effectively combined with the BRB, the knowledge is reasoned by using the evidential reasoning(ER) algorithm, the initial parameters of the model are optimized, and the optimal set of parameters is obtained,which improves the accuracy of evaluating the health status of the microswitch. Through the training and testing of the model, the experimental results show that the method can accurately evaluate the fault of the microswitch in railway vehicle, which is convenient to detect faults early, track the development trend of failure and replace the failed components in a timely manner.
引文
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[2]张龙,黄文艺,熊国良.基于多尺度熵的滚动轴承故障程度评估[J].振动与冲击,2014,33(9):185-189.(Zhang L,Huang W Y,Xiong G L.Assessment of rolling element bearing fault severity using multi-scale entropy[J].J of Vibration and Shock,2014,33(9):185-189.)
[3]Yao Dechen,Jia Limin,Ji Changxu,et al.Fault diagnosis and classification in urban rail vehicle auxiliary inverter based on wavelet packet and elman neural network[J].Jof Engineering Science and Technology Review,20136(2):150-154.
[4]廖剑,史贤俊,周绍磊,等.基于局部图嵌入加权罚SVM的模拟电路故障诊断方法[J].电工技术学报2016,31(4):28-35.(Liao J,Shi X J,Zhou S L,et al.Analog circuit fault diagnosis based on local graph embedding weighted-penalty SVM[J].Trans of China Electrotechnical Society,2016,31(4):28-35.)
[5]Verbert K,Schutter B D,Babuka R.Fault diagnosis using spatial and temporal information with application to railway track circuits[J].Engineering Applications of Artificial Intelligence,2016,56:200-211.
[6]曹愈远,张建,李艳军,等.基于模糊粗糙集和SVM的航空发动机故障诊断[J].振动、测试与诊断,201737(1):169-173.(Cao Y Y,Zhang J,Li Y J,et al.A fault diagnosis method for aircraft engine based on the fuzzy rough set and SVM[J].J of Vibration,Measurement and Diagnosis2017,37(1):169-173.)
[7]胡佳琳,张炜,操丹丹,等.基于模糊理论的电力系统健康状况的研究[J].电力系统保护与控制,201644(13):61-66.(Hu J L,Zhang W,Cao D D,et al.Research on health status of power system based on fuzzy theory[J].Power System Protection and Control,2016,44(13):61-66.)
[8]Yang J B,Liu J,Wang J,et al.Belief rule-base inference methodology using the evidential reasoning Approach-RIMER[J].IEEE Trans on Systems,Man,&Cybernetics,Part A:Systems&Humans,2006,36(2):266-285.
[9]张伟,石菖蒲,胡昌华,等.基于置信规则库专家系统的发动机故障诊断[J].系统仿真技术,2011,7(1):11-15.(Zhang W,Shi C P,Hu C H,et al.Fault diagnosis of engine based on RIMER expert system[J].System Simulation Technology,2011,7(1):11-15.)
[10]Zhou Z J,Hu C H,Yang J B,et al.Online updating belief rule based system for pipeline leak detection under expert intervention[J].Expert Systems with Applications,200936(4):7700-7709.
[11]Xu D L,Liu J,Yang J B,et al.Inference and learning methodology of belief-rule-based expert system for pipeline leak detection[J].Expert Systems with Applications,2007,32(1):103-113.
[12]周志杰,赵福均,胡昌华,等.基于证据推理的航天继电器故障预测方法[J].山东大学学报:工学版,201747(5):22-29.(Zhou Z J,Zhao F J,Hu C H,et al.Failure prognosis method based on evidential reasoning for aerospace relay[J].J of Shandong University:Engineering Science2017,47(5):22-29.)
[13]厉雷,高劭鸿,厉焕璞.开关电器可靠性失效判据与检测[J].电气开关,1995(1):3-11.(Li L,Gao S H,Li H P.Reliability failure criterion and detection of switchgear[J].Electric Switchgear,1995(1):3-11.)
[14]叶雪荣,林义刚,黄晓毅,等.航天继电器贮存过程吸合时间退化机理研究[J].电工技术学报,2017,32(11):173-179.(Ye X R,Lin Y G,Huang X Y,et al.Research on storage degradation mechanism of aerospace electromagnetic relay pick-up time[J].Trans of China Electrotechnical Society,2017,32(11):173-179.)
[15]张菲菲,李志刚,李玲玲,等.触点寿命试验中初始接触电阻与寿命的关系研究[J].电力系统保护与控制2015,43(5):63-68.(Zhang F F,Li Z G,Li L L,et al.Research on the relationship between initial contact resistance and lifetime in contact life tests[J].Power System Protection and Control,2015,43(5):63-68.)
[16]周志杰.置信规则库专家系统与复杂系统建模[M].北京:科学出版社,2011:9.(Zhou Z J.Belief rule base and complex system modeling[M].Beijing:Science Press,2011:9.)
[17]王应明,杨隆浩,常雷雷,等.置信规则库规则约简的粗糙集方法[J].控制与决策,2014,29(11):1943-1950.(Wang Y M,Yang L H,Chang L L,et al.Rough set method for rule reduction in belief rule base[J].Control and Decision,2014,29(11):1943-1950.)
[18]Bradley J M,Atkins E M.A cyber-physical optimization approach to mission success for unmanned aircraft systems[J].J of Aerospace Computing Information and Communication,2014,11(1):48-60.
[19]Li Gailing,Zhou Zhijie,Hu Changhua,et al.A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base[J].Safety Science,2017,93:108-120.