Research on intelligent fault diagnosis of equipment based on deep learning and knowledge graph

Abstract

In the running state of equipment, the accurate discovery and diagnosis of existing problems is an effective means to ensure the quality and benefit of system operation. Therefore, by using deep learning and knowledge mapping in practical exploration, researchers of various countries have put forward an intelligent fault diagnosis method based on multi-modal information of equipment, which can not only discover the hidden problems within the system in time, but also put forward effective prevention countermeasures based on the diagnosis of problems. In this paper, after understanding the knowledge graph technology and deep learning concept, a corresponding system model was constructed by extracting and integrating the collected multi-modal data information and referring to doctors' diagnosis and treatment process of patients. The final experimental results show that the system can diagnose the equipment autonomously and effectively improve the efficiency of daily management of the system.