Affiliations: Chongqing Industry Polytechnic College, School of Mechanical Engineering and Automation, Yubei District Chongqing, China
Correspondence:
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Corresponding author. Yi Zheng, Chongqing Industry Polytechnic College, School of Mechanical Engineering and
Automation, Yubei District Chongqing, China. E-mail: zhengyi@cqipc.edu.cn.
Abstract: In this paper, an in-depth analysis of automated production line faults based on fuzzy algorithms is carried out and based on an in-depth investigation of the mechanism of equipment faults, research work on equipment state prediction and production line fault diagnosis is carried out, and the corresponding algorithm model workflow is given, which has some practical application value for improving the accuracy of production line fault prediction. The algorithm with data mining association rules is proposed to extract the confidence parameters of the conditional state fuzzy net model, and an inverse conditional state fuzzy net is established based on the conditional state fuzzy net for fault diagnosis and reasoning, and a dynamic confidence level reasoning mechanism is also established for reverse reasoning based on the iterative algorithm of maximum algebra. To monitor the operating status of the production line more intuitively, a production line fault prediction and analysis system is developed based on the platform, which mainly includes a data management module, state monitoring module, state prediction module, fault diagnosis module, and maintenance advice module, which can more easily realize the monitoring of the production line equipment state and fault early warning prompting, making the system more practical value.
Keywords: Fuzzy algorithms, automation, production lines, failure analysis
