Application of the Association Rule of Data Mining Technology to Equipment Failure: Using Firm A as an Example

• Main Authors: LIN, FU-SEN, 林富森
• Other Authors: LU, HI-WEN
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/faqg2d

碩士 === 南華大學 === 資訊管理學系 === 106 === 　　Various increasing costs in the textile industry, which is a type of traditional manufacturing industry, have resulted in the relocation of some Taiwanese factories to countries with lower wage costs. Internal factors such as yearly depreciation of aging factory machinery, frequent machine failures, and a decrease in equipment reliability have heavily impaired production efficiency and resulted in increased operating costs. Because information technology rapidly presents new developments, valuable knowledge regarding rapid data collection methods, uncovering potentially useful data, and extracting useful information has formed the field of data mining. The goals of data mining on textile production data are to find the correlations of machine failures, reduce the frequency of such failures, and enhance production efficiency. 　　This paper is a case study that examines a firm’s equipment failure records, and applies data mining technology in the form of the Apriori algorithm to analyze equipment failure attributes and identify highly significant rules. This study provides suggestions for improvement to minimize the frequency of machine failures.