局部放電的產生可能會導致電力設備故障甚 至燒毀的情況，因此局部放電檢測對於維護電力設備品質的重要性非常大。本論文主要是利用音射感測器(AE sensor)放置在旋轉電機外殼上，擷取旋轉電機之異常訊號，將紀錄的完整波形透過兩種小波分析方法，分別為小波包分解法(Wavelet packet)和小波濾除雜訊法(Wavelet denoise)，把環境雜訊濾除只留下瑕疵訊號的脈衝訊號，並利用FFT分析其頻率分佈範圍，再透過立體圖譜分析找出其瑕疵訊號的特徵，建立一套區別旋轉電機不同損壞部位的辨識系統。實驗主要針對旋轉電機的軸承、轉子和定子三種不同部位進行損壞模擬和分析辨識，並用多台相同型號的旋轉電機來驗證，以確認分析結果是否有一致性。本論文所開發的檢測系統能大大降低檢測成本，並幫助檢測人員當作一個判斷損壞的參考依據。

In this thesis, an acoustic emission (AE) sensor was used to capture signals from defects in rotating electrical machines. The captured signals are often blurred by noises; therefore, to identify the defect signals, they are differentiated from noise by using wavelet analysis (WA) methods. The signal processing system developed in this study incorporates two types of wavelet analysis methods: the wavelet packet method and wavelet denoise method. In addition, fast fourier transform (FFT) is adopted to obtain the frequency distribution of defect signal data, and the defect signals are displayed in 3D space by using PD patterns. An identification system that can distinguish the defective parts of rotating electrical machines is constructed. The effectiveness of the system is analyzed by comparing the experimentally measured defect signal data from three defective parts of rotating electrical machine (e.g., the rotor, stator and shaft). The consistency of the analysis results is verified by conducting experiments on several rotating electrical machines.

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