混合信息熵约束下的电源车传感器优化配置方法

doi:10.12382/bgxb.2021.0478

摘要/Abstract

摘要： 为提升电源车系统的故障可诊断性能，提出一种基于混合信息熵指标约束的电源车传感器优化配置方法。故障可诊断性低是导致电源车故障难以排除的主要原因之一，而测点传感器配置不均衡则是难以迅速、可靠检测电源车故障的本征所在。为此，利用信息值熵理论，借助贝叶斯定理获取电源车系统满足常见故障可诊断性所需的传感器配置集合；考虑到不同传感器之间可能存在冗余，会使得传感器在数量和位置上的配置难以达到最优，进一步借助传递熵方法量化传感器间的冗余度，并将传感器的优化配置问题归结为求解信息值和传递熵的多目标优化问题，以此来获取最佳的传感器配置集合。仿真实验表明，混合信息熵方法对于明确和解析传感器在故障诊断过程中最大的覆盖空间和最小的配置集合具有明显优势。

关键词: 信息值, 传递熵, 故障可诊断性, 传感器配置, 电源车

Abstract: An optimal sensor deployment method based on hybrid information entropy index is proposed to improve the fault diagnosis performance of power supply vehicles (PSVs). Low fault diagnosability of PSVs is one of the main causes of their high failure rate, and the unbalanced configuration of the measuring point sensors is the key to the difficulty of rapid and reliable detection of power vehicle faults. Thus, we can first obtain the posterior probability of sensor residual after fault using the Bayes theorem, and then calculate the Value of information (VOL) of the posterior probability for PSV fault diagnosis. Second, due to the possibility of redundancy between sensors, it is difficult for sensors to interpret the numbers and locations simultaneously. Therefore, the redundancy between sensors is further quantified by using the Transfer Entropy (TE) method, and the optimal sensor configuration problem is reduced to a multi-objective optimization problem of solving VOL and TE, which reveals the best sensor configuration. Simulation results show that the hybrid information entropy method has obvious advantages for clarifying and analyzing the maximum coverage space and the minimum configuration set of sensors in the process of fault diagnosis.

Key words: valueofinformation, transferentropy, faultdiagnosability, sensorplacement, powersupplyvehicle

中图分类号:

TP277

蒋栋年，李炜. 混合信息熵约束下的电源车传感器优化配置方法[J]. 兵工学报, 2022, 43(8): 1763-1771.

JIANG Dongnian， LI Wei. Optimal Sensor Deployment for Power Supply Vehicles under Hybrid Information-Entropy Constraints[J]. Acta Armamentarii, 2022, 43(8): 1763-1771.

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