預測性維護的重要性在近年來得到了廣泛的重視，因為機器的意外崩潰會造成很多損失，而且通常需要大量的時間來製造和運送維修的部件。因為對於公司來說，儲存每一個可能需要的部件是不切實際且不符合經濟效益的，所以預測性維護在此時可以起到很大的幫助作用。
然而，當處理從機器上檢測到的時間序列數據時，傳統的統計方法通常會產生巨大的計算費用，並花費大量時間，但只能提供不精確的結果。因此，本研究提出了一種提高性能的方法，它將遺傳算法應用於集合學習，同時將MiniRocket從時間序列數據中提取的特徵作為輸入，希望以此來改善分析的結果。
本研究使用了一個真實世界的數據集作為案例，該數據集由台灣一家提供電線和電纜的知名公司提供。本研究所提演算法的性能由四個指標來予以衡量，實驗結果顯示採用遺傳演算法為基礎的集成演算法較單獨使用個別演算法，可產生較佳的結果。

The importance of predictive maintenance has been valued recently, because the unexpected crash of machine would cause many losses and it usually takes a lot of time to manufacture and ship the components for repairing. Since it is unrealistic and not economical for company to store every component it may need, predictive maintenance can be helpful.
However, when dealing with time series data detected from machines, the traditional statistical methods usually generate huge computational expenses and take enormous time but can only provide imprecise results. Therefore, this study proposes a method to improve the time series classification performance. It applies a genetic algorithm (GA) to ensemble learning while takes features extracted from time series data by MiniRocket as input.
Besides the benchmark datasets, a real-world application is also used in the current study, which is offered by a well-known company providing wires and cables in Taiwan. The proposed algorithm's performance is measured by four indicators. According to the experiment results, employing GA for ensemble learning method is able to yield better outcomes than utilizing individual algorithms alone both for benchmark and case study datasets.

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