关键词: 工业知识图谱, 因果知识建模, 设备点检故障, 根因分析

Abstract: Equipment spot-inspection records are essential information sources supporting fault reason analysis.However,there is a lack of effective mining of root cause information in equipment spot-inspection failures to improve the reliability of preventive equipment maintenance.Therefore,the causal theory was introduced into the manufacturing field for the first time to propose a causal knowledge modeling for root cause analysis of equipment spot-inspection failure.The event knowledge was extracted from the equipment spot-inspection fault documents to construct a fault operation and maintenance causal knowledge graph.The fault operation and maintenance causal knowledge rules were defined to establish a structural causal graph model.Then,an Intervention Sum Product Networks (ISPN)-based causal effect estimation learning model was designed to calculate the confounding influences in the fault knowledge and determine the main factors affecting equipment failures.Moreover,the implicit causal semantic links between nodes were complemented.The proposed method was tested with knowledge of metallurgical equipment spot inspection failure documentation.The feasibility of the causal knowledge model in estimating the causal effect among knowledge of the root causes for equipment failures was verified.

Key words: industrial knowledge graph, causal knowledge modeling, equipment spot-inspection faulty, root cause analysis