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Fault diagnosis of belt conveyor drive system based on RBR+CBR dual reasoning

Published:2023-11-27 author:ZHANG Yu, KOU Ziming, HAN Cong, et al. Browse: 1067 Check PDF documents

Fault diagnosis of belt conveyor drive system based on RBR+CBR

dual reasoning

ZHANG Yu1,2, KOU Ziming1,2, HAN Cong1,2, KOU Shaokai1,2

(1.College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China;

2.National and Local Joint Engineering Laboratory for Mine Fluid Control, Taiyuan 030024, China)

Abstract: Both rulebased reasoning (RBR) and casebased reasoning (CBR) methods are applicable to the field of belt conveyor fault diagnosis. Aiming at the problem that both of them focus on cause analysis or maintenance means in fault diagnosis, the reasoning mechanism based on RBR+CBR dual reasoning was studied, and an expert system was established to diagnose the fault of belt conveyor drive system. Firstly, the expert system for fault diagnosis was constructed on the basis of the structure of traditional expert system. Then, the fault tree of the belt conveyor drive system was established according to the fault mechanism, the expert system fault rule base was designed by using the generative representation method, and according to the fault cases and expert experience, the expert system fault case base was designed by using the matrix representation method. The expert system reasoning mechanism was designed based on the rulebased reasoning (RBR) and case-based reasoning (CBR) methods. And the human-computer interaction interface of the expert system was designed by using the LabVIEW program development platform, which realized the series communication process between the sensor, knowledge base and client. Finally, in order to verify the feasibility of the above expert system, experiments were conducted on a belt conveyor operating site in a certain mine, and the results were analyzed. The results of the study show that,the fault cause analysis accuracy of the belt conveyor expert system exceeds 95%, and the effectiveness of the fault repair plan of the expert system exceeds 94%. In comparison with the expert system supported by RBR and CBR algorithms, this expert system has better convergence, effectiveness and accuracy, and it proposes an effective method for the intelligent fault diagnosis of the belt conveyor.

Key words: transportation machinery; belt conveyor; fault diagnosis expert system; expert system reasoning mechanism; fault diagnosis accuracy; rule-based reasoning (RBR); case-based reasoning (CBR)

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