Technical gazette, Vol. 27 No. 2, 2020.
Original scientific paper
https://doi.org/10.17559/TV-20190804140256
Research on Fault Diagnosis Based on Dynamic causality diagram and Fuzzy Reasoning Fusion Method
Yinkai Wang
; School of Electrical and Information Engineering, Tianjin University, China TianJin Special Equipment Inspection Institute, China
Hongguo Chen
; TianJin Special Equipment Inspection Institute, China
Zhiming Zhan
; TianJin Special Equipment Inspection Institute, China
Abstract
With the progress of urbanization, the demand for elevators has upgraded from safe operation to comfortable, efficient, and all-round demand. The abnormal operation of the elevator is difficult to diagnose due to the complexity of the fault. This paper proposes a fault diagnosis method based on dynamic causality diagram and fuzzy reasoning. The dynamic causality diagram is extended, the intermediate module nodes are added, the description of the intermediate process of the elevator control system is solved, and the complete expression of knowledge is realized. The control timing of the elevator operation is introduced into the network structure of the dynamic causality diagram, which enhances the dynamic characteristics of the network. The causal cycle logic of the dynamic causality diagram is used to represent input and output signals and faults in elevator control systems. In the update of fuzzy rules, the real-time of fuzzy reasoning is enhanced, the search space of fuzzy rule matching is reduced, and the efficiency is improved. This paper combines actual field measurements and experimental data for fault diagnosis. Finally, the simulation, diagnosis and maintenance decision of the fault are realized, and an intelligent solution for elevator fault diagnosis is further proposed.
Keywords
dynamic causality diagram; elevator; Fault Diagnosis; fuzzy reasoning; safe operation
Hrčak ID:
236792
URI
Publication date:
15.4.2020.
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