改进动态因果图与模糊推理融合故障诊断方法

Improved Dynamic Causality Diagram and Fuzzy Inference Fusion Fault Diagnosis Method

在复杂的控制系统发生故障时,运维系统能保证对其进行快速、可靠的故障诊断尤为重要。针对复杂控制系统中控制信号多、信号关联性强、故障状态多、部件故障模式多的情况,提出一种改进动态因果图与模糊推理融合的故障诊断方法,利用改进动态因果图逻辑表达能力强,能因果互推的特点,构建多重(正向、反向、混合)模糊规则,有效克服了模糊逻辑推理中只能由因溯果而不能由果溯因的难题,同时,将动态因果图的动态特性引入到模糊规则的动态更新中,增强了模糊推理的实时性。最后,以某型装甲设备垂直力矩电机控制过程的故障诊断为应用背景,在自行研制的故障诊断平台中嵌入此法进行故障诊断测试,测试结果分析表明,此法能有效提高诊断效率,具有更高的准确性、先进性、适用性。

The faults can be quickly and reliably diagnosed is important in the operation and maintenance system when a complex control system fails.Aiming at the situation of complex control system with many control signals,strong signal correlation,many signal states and many component failure modes,this paper proposes a fault diagnosis method based on improved dynamic causal graph and fuzzy reasoning,which uses improved dynamic causal graph logic expression.Strong ability,can make multiple(positive,reverse,mixed)fuzzy rules based on the characteristics of causality,effectively over-come the problem that fuzzy logic reasoning can only be caused by the retrospective effect,and at the same time,the dynamic characteristics of causal graphs are introduced into the dynamic update of fuzzy rules,which enhances the real-time performance of fuzzy inference.Finally,the fault diagnosis of the vertical torque motor control process of a certain armored equipment is used as the application background,embedded in the self-developed fault diagnosis platform.This method is used for fault diagnosis test.The test results show that this method can effectively improve the diagnostic efficiency and has higher accuracy,advancement and applicability.