关键词: 重型机床, 故障诊断, 专家系统, 故障树分析

Abstract: Considering the complexity of heavy machine structure and fault, the faults of heavy machine was divided into three levels, and the concepts of “rule diagnosis body” and “case diagnosis body” were introduced. The knowledge organization mode of “structure fault tree” was evolved based on the product structure tree. And then mapping between rule diagnosis bodies and frame representation were used to describe fault association and fault case respectively. Also, the organization of database was put forward. According to rules characteristics of heavy machine, the rule-based diagnosis was implemented by Back Propagation (BP) nural network trained by the information of rule diagnosis bodies. Besides, the case-based reasoning diagnosis based on case diagnosis body converted to fuzzy membership was proposed as well, in which matching retrieval of to-be-solved case was performed through the nearest neighborhood algorithm. Case reuse and case adjustment were introduced. Aiming at case-base management, new cases were stored by K nearest-neighbor algorithm based on TC similarity. The construction process of original sample cases was introduced through “classification of associations-verification by clustering-storing after adjustment”. Finally, prototype system was developed.

Key words: heavy machine, fault diagnosis, expert system, fault tree analysis