Construction and management of industrial system failure mode knowledge graph

Abstract

In the process of industrial production and to ensure the production order, it is necessary to monitor the process in real time, detect errors and take action in advance to reduce losses. Failure Mode and Effects Analysis (FMEA) is a systematic activity to analyze product modules, parts and various operations in the production process to identify potential failure modes and analyze their possible consequences. This leads to necessary actions being taken in advance to improve product quality and reliability. Efficient management of FMEA data is beneficial for controlling the production process and improving production quality. Based on the failure mode and effects analysis (FMEA) data of industrial systems, this paper builds a knowledge graph of failure modes and designs, and develops the corresponding modules for the management functions, including knowledge graph creation, knowledge graph storage, and knowledge graph retrieval. First, the ontology structure of the failure mode is designed in terms of the failure mode of industrial systems. Second, the facts are extracted from the unstructured data in FMEA, the structured data is cleaned, the abnormal data is eliminated, and the missing data is recovered. Third, according to the correlation between the pattern level ontology, the knowledge graph triplet is created and the FMEA knowledge graph is constructed; then the storage function of the FMEA knowledge graph is designed and implemented based on the graph database neo4j; finally, the KNN algorithm for the similarity search in the FMEA knowledge graph is proposed.