电源车传感器故障检测和数据重构方法  

Sensor fault detection and data reconstruction method of power supply vehicle

作　　者：蒋栋年[1,2] 把余江 李炜 JIANG Dongnian;BA Yujiang;LI Wei(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;State Grid Gansu Electric Power Research Institute,Lanzhou 730050,China;Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学电气工程与信息工程学院,兰州730050 [2]国家电网甘肃省电力科学研究院,兰州730050 [3]兰州理工大学甘肃省工业过程先进控制重点实验室,兰州730050

出　　处：《北京航空航天大学学报》2023年第7期1583-1592,共10页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(62263020);甘肃省杰出青年科学基金(20JR10RA202);兰州理工大学红柳优秀青年人才资助计划;兰州市科技计划(2022-2-69)。

摘　　要：针对电源车由于运行环境复杂而容易发生传感器故障的问题,提出了一种基于时空相关性的传感器故障检测和数据重构方法。针对单个传感器运行数据的时序关系特征,借助具有选择与遗忘机制的极限学习机(SF-ELM)建立了电源车传感器时间序列预测子模型,并据此实现对电源车传感器的故障检测;针对已检测的故障传感器,利用不同传感器之间的空间相关性,通过冗余度分析,使用改进后的互信息熵筛选出与故障传感器数据相关性较高的辅助传感器数据,实现对故障传感器失效数据的在线重构;通过仿真验证了所提方法在电源车传感器故障检测和数据重构中的可行性与有效性。Aiming at the problem that the power supply vehicle is prone to sensor fault due to the complex operating environment,a sensor fault detection and data reconstruction method based on spatiotemporal correlation is proposed in this paper.Firstly,according to the time-series relationship characteristics of single sensor operation data,the fault detection sub-model of the power vehicle sensor is established with the help of the selective forgetting extreme learning machine(SF-ELM)mechanism,and the fault detection of power vehicle sensor is realized.Secondly,simultaneous interpreting the fault sensors,using the spatial correlation among different sensors,and through redundancy analysis,the improved mutual information entropy is used to screen out the auxiliary sensor data which is highly correlated with the fault sensor data,and the online reconstruction of the failure sensor data is realized.Finally,the feasibility and effectiveness of the proposed method in sensor fault detection and data reconstruction of power vehicles are verified by simulation.

关 键 词：传感器 时空相关性 极限学习机 故障检测 数据重构 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]