近幾年政府正在推動工業4.0，而工業4.0講的就是智慧化工廠，智慧化工廠其中最重要的一點就是預測性維修，本研究提供了一個預測性維修(包含狀態監測、故障診斷、狀態預測和維修決策)建立的方法與流程，此預測性維修結合了Jiang et al. (2015) 所提到Maintenance Priority Number(MPN)的概念與支持向量回歸(Support Vector Regression)，並在最後運用 Van Horenbeek and Pintelon (2013) 所提到的衰退模型進行模擬，模擬在相同情況與時間下使用Jiang et al. (2015)的預防性維修以及本研究的預測性維修分別造成系統多少停機次數、維修次數、維修成本，並將兩結果進行比較；結果發現在模擬案例一中預測性維修的維修總成本比預防性維修的維修總成本平均少了26.7%，預測性維修在停機次數上低於預防性維修，在維修次數上兩者無太大差距；在模擬案例二中預測性維修在元件壽命改變的狀況下表現出良好的資料即時更新能力，在停機次數上少了預防性維修8次。

Government of Taiwan is promoting Industry 4.0 as its new industrial policy in recent years, which is aiming at the construction of Smart Factory. Predictive maintenance is one of the most crucial elements of Smart Factory. This study proposes a methodology and a procedure for predictive maintenance which includes Condition Monitoring, Fault Diagnostics, Prognostic Condition and Maintenance Decision-Making. This method is a combination of Support Vector Regression and the concept of Maintenance Priority Number (MPN) referred by Jiang et al. (2015). In the last part of this study, we simulate the practice of two methods of preventive maintenance, the preventive maintenance of Jiang et al. (2015) and the predictive maintenance of this study, with Degradation Modeling (Van Horenbeek and Pintelon,2013), and make a comparison of their results including the number of downtime, the number of maintenances and maintenance costs. In case one, we found that the total cost of maintenance of the predictive maintenance is 26.7% less than that of maintenance of the preventive maintenance, and that the number of downtime of predictive maintenance is fewer than the preventive maintenance. There was no significant difference in the number of maintenances. In case two, the predictive maintenance demonstrates its strong ability of data updating in the situation that the life of the component is changing. The number of downtime of the predictive maintenance is eight times fewer than that of the preventive maintenance.

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