基于D-S融合的混合专家知识系统故障诊断方法

A Fault Diagnosis Approach by D-S Fusion Theory and Hybrid Expert Knowledge System

复杂流程工业过程知识类型多样且含有多种不确定性,针对这些问题提出一种基于D-S融合的混合知识系统故障诊断方法.根据可利用信息的类型建立不同的专家知识系统并进行不确定性推理.通过分析当前信息的数据特点,自适应分配不同专家知识系统可靠性权重,通过权重D-S证据理论融合各专家知识系统的结论.这种方法不仅使用了专家的知识和经验,而且结合了生产过程积累的大量数据信息,提高了信息的利用率.通过融合多个专家知识系统的结论,提高了不确定性系统故障诊断的正确率.将该方法应用于某湿法冶金浓密过程故障诊断,取得了良好的诊断效果.

There are various types of process knowledge and multiple uncertainties in complex process industry. To address these issues, a fault diagnosis approach which employs D-S knowledge fusion and hybrid knowledge system is proposed. Based on the types of available information, we establish different expert knowledge systems and present uncertainty reasoning respectively. By analyzing the characteristics of the current available data, adaptive weights are calculated for different expert knowledge systems. Then D-S evidence theory is utilized for conclusion fusion. Not only the expert experience knowledge but also a large amount of accumulated data is utilized in this method, which improves the utilization rate of information. The fault diagnosis accuracy for uncertainty systems are increased by the use of D-S conclusion fusion. The proposed method is then applied to fault diagnosis of a thickener in a hydrometallurgy process and satisfactory diagnosis results are achieved.