Mechanical equipment health management method based on improved intuitionistic fuzzy entropy and case reasoning technology

Abstract

Management becomes more challenging as machinery becomes more widely used. From the health management history case records of mechanical devices, we can find that there are many health problems of the same type occurring repeatedly, but when similar problems occur again, solutions are not found in a short period. Case-based reasoning technology aimed at solving the above problems are widely used in equipment health management, but there are problems such as inadequate data utilization and failure to achieve the required accuracy and reliability. To uncover important information from historical failure case data and help reduce equipment downtime due to failure, this paper proposes a machinery equipment health management method based on improved intuitionistic fuzzy entropy and case reasoning technology. Firstly, the axiomatic definition of traditional intuitionistic fuzzy entropy is optimized and a new intuitionistic fuzzy entropy formula in the framework of case-based reasoning decision matrix is proposed. Secondly, the weight value of each attribute is obtained by combining the formula of the entropy weight method. Finally, the feature attribute weight values are fused into the case-based reasoning, and the historical cases that are most similar to the target cases are obtained by combining the existing case base. The effectiveness of the method was verified by comparing and analyzing the example calculation results with the traditional method. The method improves the management of machinery equipment operation and maintenance data. Furthermore, it helps enterprises to carry out the management and analysis of machinery equipment health problems.