在工業領域中，旋轉電機的應用十分普遍，而因人為因素或設備老化導致的局部放電現象可能會導致電力設備故障，因此局部放電檢測對於保障電力設備的品質至關重要。本論文同時以高頻電流感測器(HFCT)、超高頻感測器(UHF)與暫態對地電壓感測器(TEV)，量測在業界已運轉多年的旋轉電機之局部放電訊號。以三種感測器擷取的原始訊號會經由類比帶通濾波器(Band-pass Filter)與小波閾值降噪法(Wavelet Threshold)將雜訊濾除，留下局部放電訊號，並利用快速傅立葉轉換分析局部放電訊號的頻率分佈範圍。接著，將擷取到的訊號之相位與振幅繪製出局部放電圖譜，可藉由局部放電圖譜觀察局部放電訊號發生之相位與次數。最後從局部放電圖譜中提取出放電特徵，並搭配旋轉電機近期的電氣絕緣數據，將此雙重特徵輸入至雙流神經網路(Dual-stream Neural Network)模型後，推斷出旋轉電機絕緣系統的健康程度指數。綜合上述方法，可提高電力設備的可靠性和安全性，給予檢測人員一個判斷電力設備是否需下線維修的參考依據。

Rotating electrical machines are crucial in various industries. Detecting partial discharge (PD) is essential to ensure the quality and reliability of power equipment, as it helps prevent potential failures caused by human factors or equipment aging. In this thesis, High-Frequency Current Transformers (HFCT), Ultra High-Frequency (UHF) sensors, and Transient Earth Voltage (TEV) sensors were used to capture PD signals in rotating electrical machines that have been operational for several years.
　The signals collected by the three sensors are first processed using band-pass filters and then denoised using the wavelet threshold denoising method to remove any noise, leaving only the PD signals. The frequency distribution of these signals is then analyzed using Fast Fourier Transform (FFT). The phase and amplitude of the collected PD signals are utilized to create PD patterns, enabling the observation of the phase and frequency of the PD occurrences. These patterns are used to extract discharge characteristics, which are then combined with the most recent electrical insulation index from electrical rotating machines.
　These combined features are input into a dual-stream neural network model to determine the health index of the insulation system. Integrating these methods enhances the reliability and safety of power equipment, providing maintenance personnel with a reference for deciding whether the electrical equipment needs offline maintenance.

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