Research on knowledge graph-driven equipment fault diagnosis method for intelligent manufacturing

In the process of rotating machinery fault diagnosis (RMFD), the lack of feature conditions leads to the problem of low accuracy of traditional rule-based reasoning methods FD. This paper proposed a knowledge graph (KG)-driven device FD method and applied it to RMFD. First, we proposed a multi-level KG construction method to get multi-source data based on each level and analyzed the levels that affected the system state. A single-level KG was constructed through data features, and a multilevel KG with a stereostructure was built using a multi-source data fusion model as data support for FD. Second, we proposed an approach based on multilevel KG and Bayes theory to detect the system state and located the source of faults by combining the KG reasoning based on relational paths, then used the relationships between the structures of rotating mechanical equipment for fault cause reasoning and used the KG as a knowledge base for a reason using machine learning. Finally, the proposed method was validated using a steelworks motor as an example and compared with other ways, such as rule-based FD. The results show that under the condition of missing input features, the accuracy of the proposed method reaches 91.1%, which is significantly higher than other methods and effectively solves the problem of low diagnostic accuracy.