Equipment Condition Monitoring and Diagnosis System Based on Evidence Weight

A system for monitoring and diagnosing the working conditions of equipment is developed in this study to ensure equipment safety and reliability. First, a perceptual model with four layers is designed. Original data are collected by sensors, and analyses are performed with intelligent algorithms. Decisions are then made and displayed on a screen in real time. Second, a method for monitoring equipment conditions is developed based on evidence weights. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. Third, the effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of centrifugal pumps. Finally, the system is implemented to verify its validity.