An Intelligent Framework of Equipment Fault Diagnosis Based on Knowledge Graph

Equipment fault information typically exhibits the characteristics of fragmentation and diversified structure. The existing fault diagnosis methods are incapable of fully exploiting the prior knowledge and expert knowledge within the field, and the diagnosis results are overly one-sided. Given that it is challenging to obtain effective fault diagnostic knowledge for complex equipment and complex faults, this paper proposes the application of the domain knowledge graph (KG) for fault diagnosis. Different from the existing fault diagnosis methods involving the KG, our framework consists of two major parts. The first part is to construct an equipment fault KG from semi-structured and unstructured text. We further enrich the graph knowledge through knowledge completion, which can furnish high-quality knowledge sources for downstream applications. The second part is to employ the built fault KG either online or offline for fault diagnosis. We offer two approaches: the deep learning plus KG approach; the question-answering approach. The former not only guarantees the diagnostic accuracy but also provides more comprehensive diagnostic information. This constitutes the online utilization of the KG. The latter realizes the offline use of the KG, providing users with a natural and user-friendly manner to retrieve fault diagnosis information. We demonstrate and verify our proposed framework in the context of the bearing fault diagnosis.