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

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.