基于证据推理规则的信息融合故障诊断方法

本文针对不确定性故障特征信息的融合决策问题,给出基于证据推理(evidence reasoning,ER)规则的故障诊断方法.首先基于故障特征样本似然函数归一化的方法求取各传感器(信息源)提供的诊断证据;从传感器误差以及故障特征对各故障类型辨别能力的差异出发,给出获取诊断证据可靠性因子的方法;给出双目标优化模型训练得到诊断证据的重要性权重,最后利用ER规则融合经可靠性因子和重要性权重修正后的诊断证据,利用融合结果进行故障决策.该方法继承了Dempster-Shafer证据理论处理不确定性信息融合问题的优点,同时克服了它在实际应用中无法区分证据可靠性和重要性的不足,使得所获诊断证据更为客观、可信.最后,通过在多功能电机转子试验台上的故障诊断实验,验证了所提方法的有效性.

Information fusion method for fault diagnosis based on evidential reasoning rule

This paper presents an evidential reasoning (ER)-based method of fault diagnosis by combining uncertain information of various fault features collected from multiple sources for fault decision-making. A normalization approach is applied to acquire diagnosis evidence from the likelihood function of fault feature samples gathered from information sources (sensors). A novel method is proposed to calculate evidence reliability according to sensor accuracy specifications and the differences of capabilities in recognizing fault modes through different fault features. A bi-objective optimization model is presented to train evidence weights to reflect the relative importance of evidence. The ER rule is then applied to combine multiple pieces of diagnosis evidence, which are regulated by their weights and reliability factors, and fault decision-makingcanthusbeconductedonthebasisofthecombinedresults. TheproposedER-basedfaultdiagnosismethod inherits the main features of Dempster-Shafer evidence theory in uncertainty modelling, while providing a systematic process for explicitly taking into account the reliability and importance of evidence, thereby enabling rigorous inference and robust decision making. Finally, a diagnosis experiment on a rotor test bed is conducted to show the effectiveness of the proposed ER-based fault diagnosis method.