Leveraging on causal knowledge for enhancing the root cause analysis of equipment spot inspection failures

Causal correlation data over the equipment spot-inspection operation and maintenance (O&M) records and fault investigation sheets potentially reflect the state related to the causal effect of equipment failures. Various factors influence equipment failures, making it difficult to effectively analyze the main cause of the problems. Mining and leveraging these causal data from the equipment spot inspection records will undoubtedly significantly improve the root cause analysis of the fault in the O&M system. Hence, this paper introduces causal knowledge in equipment fault O&M for the first time and proposes to exploit causal knowledge for enhancing root cause analysis of equipment spot inspection failures. Specifically, an equipment fault O&M knowledge graph with causal knowledge called CausalKG is constructed to provide knowledge support for the causal analysis of faults. That is, CausalKG consists of spot-inspection knowledge graph (SIKG) and causal relationship knowledge (CRK) in equipment fault O&M. Further, a CausalKG-ALBERT knowledge reasoning model is designed. The model transforms CausalKG into network embeddings based on relational graph convolutional networks. In turn, it combines the Q&A mechanism of the language model ALBERT to mine the root cause knowledge of equipment failures. The case study confirms that incorporating the CRK is more effective than directly using the SIKG for causality reasoning; The model can fully use causal relationship knowledge to enhance the reliability of root cause analysis. This method is valuable to help engineers strengthen their causal analysis capabilities in pre-ventive equipment maintenance.