Recommendation and Election Expert System for Rotating Machinery Fault Diagnosis Based on the Combination of Rules and Examples

Abstract

Energy internet needs a comprehensive grasp of all power generation equipment. In order to simulate the behavior of human experts in real-time diagnosis of equipment operating status and fault types, a research on the fault diagnosis expert system of rotating machinery in thermal power plants is carried out, and a recommendation and election expert system based on the integration of rules and examples is proposed. The expert system combines traditional rule-based fault tree inference with case-based inference, and proposes a stepped inference strategy through online elections, which can perform online real-time fault diagnosis based on signals such as vibration and speed to improve the accuracy of fault diagnosis.