近年來環保能源意識抬頭，隨著「2025年綠電比例達20%」的政策推行，許多綠色能源發電產業如雨後春筍般出現。2019年台灣離岸風電產業協會的成立，更凸顯了台灣離岸風電的發展潛力與重要性。離岸風場設備所需的維護成本較陸地風場高，而離岸風機的零件保養維護也隨之成為產業優化的研究焦點。近幾年來，人工智慧(artificial intelligence，AI)技術被廣泛應用於故障預測和健康管理(prognostic and health management，PHM)。此外，結合工業物聯網(industrial internet of things，IIoT)架構，巨量設備監控資料可藉由數據採集與監控系統(supervisory control and data acquisition，SCADA)進行傳輸，如何應用此巨量資料預測維護診斷設備之故障，對於設備的維護成本有著舉足輕重之影響。本研究之目標在於導入決策樹分類、K-近鄰分類器、羅吉斯迴歸分類器和支持向量機至預測性維護分析(predictive maintenance，PdM)，探討不同的機器學習演算法對於相關馬達資料集之運算結果，實驗結果顯示機器學習演算法可達95%以上準確度。本論文對於機器學習演算法的預測性分析能力提供了初步探討，可供未來風機設備的馬達預測性維護分析之參考。

In recent years, the increase of green energy awareness has risen with the policy to “20% of green electricity in 2025.” Many green energy companies have sprung up. The establishment of the Taiwan Wind Industry Association in 2019 further highlights the development potential and importance of offshore wind energy in Taiwan. As the maintenance cost of equipment in the offshore wind farm is higher than that of the onshore wind farm, the fault prediction and health management of offshore wind turbines have become important research. In recent years, Artificial Intelligence (AI) has been widely used in Prognostic and Health Management (PHM). By equipment monitoring with the Industrial Internet of Things (IIoT) architecture, huge amounts of data collected can be transmitted through Supervisory Control and Data Acquisition (SCADA) system. How to apply this big data for PHM has a decisive influence on the maintenance cost of equipment in the offshore wind farm. The goal of our study is to import and test some machine learning algorithms, including decision tree classifier, K-nearest neighbor classifier, logistic regression classifier, and support vector machine, for Predictive Maintenance (PdM). The experimental results show that the accuracy is over 95%. It provides a preliminary discussion on machine learning algorithms for a possible reference to PdM analysis of wind turbines in the future.