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Weapon systems require reliability in the development phase for efficient combat readiness. Improved reliability in various manufacturing processes have been achieved using data analysis. However, data analysis in the development phase is difficult due to problems such as the lack of data, high cost, and the importance of security. Therefore, Post Logistics Support (PLS) data collected following integration is analyzed for long-term quality improvement of weapon systems. In this study, we propose a methodology for examining the correlation between the failure rate and PLS data as follows: First, key variables affecting reliability were identified the correlation between variables on the failure rate examined. Second, corresponding analysis was conducted for determining the correlation between patterns of categorical data. Third, extract categories with the higher contribution and quality of representation, and find the highest variable correlated with failure period through visualization. Then, after selecting patterns which have shorter failure period, the cause of decreased reliability was confirmed through frequency analysis. This study will contribute to improving reliability when developing new weapon systems and will help to strengthen the combat readiness of military.

The research of Correspondence Analysis centered on the Failure Period to improve the reliability of Weapon Systems