Abstract
To ensure the safety and reliability of equipment and effectively prevent the occurrence of major accidents, a monitoring and diagnosis system of equipment condition is proposed in this paper. First, a perceptual model of four layers, which can collect the original data of equipment by sensors and analyze the real-time information with intelligent algorithms and display the decision making on the screen, is designed. Second, a method of condition monitoring and diagnosis based on evidence weight is proposed. The basic probability assignment of evidence is corrected by Mahalanobis distance and sensor weight. A threshold is introduced to select the appropriate fusion rule by comparing the relationship between threshold and conflict factor. In order to verify the effectiveness and practicability of the method, the improved fusion algorithm is applied to the monitoring and diagnosis of centrifugal pumps. Finally, a prototype system is implemented to illustrate the validity of the system in practice.
Highlights
The computing speed, level of automation, and precision of mechanical equipment have improved significantly thanks to the technological advancements in the past few decades
Given the advantage of using evidence theory to deal with uncertainty, an equipment condition monitoring and diagnosis system was designed based on an optimized fusion algorithm in this work
A system for monitoring and diagnosing equipment conditions was designed in this study to monitor the status of mechanical equipment effectively and accurately and avoid the “isolated information” phenomenon
Summary
The computing speed, level of automation, and precision of mechanical equipment have improved significantly thanks to the technological advancements in the past few decades. The application of evidence theory in equipment condition monitoring and fault diagnosis can result in high diagnosis rates, which improve equipment reliability and ensure product quality. This method can help workers make intelligent decisions to obtain potential economic and social benefits. The approach used in [11] considers the differences between pieces of evidence and achieves improved results These previous studies either redistributed basic probability assignment (BPA) or modified fusion rules to reduce conflict. Given the advantage of using evidence theory to deal with uncertainty, an equipment condition monitoring and diagnosis system was designed based on an optimized fusion algorithm in this work. The proposed system improved fault diagnosis rates, reduced fault hazards, and ensured system reliability
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