本研究探討以機器學習方式，診斷風機葉片表面是否有損傷，針對受損之風機葉片及早進行修補，避免更換新葉片造成鉅額費用支出。本實驗收集陸地風機運轉所產生之噪音訊號，測得風速範圍4-10 m/s，共截取935組不同風機運轉噪音，將此噪音進行梅爾倒頻譜(Mel-Frequency Cepstral)訊號處理，得到風機運轉噪音之梅爾倒頻譜係數(Mel-Frequency Cepstral Coefficients, MFCC)，以此係數及其微分運算作為葉片之特徵訊號，透過支持向量機器學習(Support Vector Machine, SVM)，建構偵測並診斷風機葉片表層損傷與否之分類訓練模組，希望提高分類風機運轉噪音訊號之準確率。將所有MFCC特徵係數隨機抽取70%進行SVM模型訓練，剩餘30%樣本為測試資料。研究顯示，該偵測並診斷風機葉片損傷資訊，分類結果準確率高達約97.3%。

Due to the climate change in recent years, the development of renewable energy projects has widely been promoted worldwide to reduce greenhouse gas. Despite of the contaminated environment, people tended to build the variety of renewable power systems produced by wind, sunlight, rain and other approaches which could be easily obtained from nature. The wind turbine is one of the most popular power supplies while this generally causes defects on the surface of blades based on strong wind or harsh weather besides the ocean. Thus, in order to avoid the expensive maintenance fee and prolong wind turbine’s life, an aim of this research was to build a detected system to examine whether the condition on blade surface was failure or not. Recording the sound of wind turbines on land is the main data sources since offshore wind turbine is difficult to obtain. There are 935 data including different wind speed from 4m/s to 10m/s. In this paper, we have proposed two approaches, Mel-Frequency Cepstral Coefficient (MFCC), to extract feature from the signal noise of recorded sound of wind turbine. After obtaining the coefficient from MFCC, using a classification called Support Vector Machine (SVM), to train a model which could normally present the accuracy of the result with normal or failure rotor blades. The experimental investigation indicated that MFCC features could be used for identifying the wind turbine sounds under the various wind speed, and combining the method of SVM generated the high accuracy around 97.3 percentage which could be detected immediately. The recognizing system may be useful to improve the lifetime of wind turbine as well as keeping the cost down from maintenance fee.