Application of FMEA Cluster Analysis in Maintenance Strategy Optimization

The functional performance of military electronic equipment is constantly improving, and its applied field is becoming increasingly complex, which brings greater challenges to the maintenance and support work of equipment. In engineering practice, reliability centered maintenance (RCM) and failure mode and effects analysis (FMEA) play a crucial role in formulating maintenance strategies. In order to support maintenance personnel to quickly and accurately identify effective information in the increasingly large amount of FMEA data, and develop economically effective maintenance strategies, this paper proposes a data clustering analysis method for FMEA. Starting from the perspective of data mining and processing, K-Prototypes clustering analysis algorithm is used to classify FMEA data into different types based on its inherent attributes and similarity, simplifying analysis and statistical work. Based on the characteristics of each type of data, corresponding maintenance and improvement suggestions are proposed, providing data support and strategy support for product iteration optimization and field maintenance support work respectively.