電力設備常因絕緣材料受到電應力、溫度效應、化學反應、機械應力及周遭環境的影響下，而造成其絕緣產生劣化，導致電力設備發生故障，引起不必要的停電，因此如何達到預防性設備故障診斷之目的，一直為研究之重要課題。
本文選用自製瑕疵的電纜終端接頭進行高壓劣化試驗，並量取其局部放電訊號，再利用偏態、峰態與交叉相關因素的方式，萃取出地下電纜絕緣劣化的特徵值，這些特徵值被結合並當成類神經網路的輸入訊號，以達到絕緣劣化辨識的效果。
結果顯示，利用偏態及峰態的組合為輸入訊號時，其辨識成功率約可達到92%，顯見該組合確具劣化辨識之效果，在地下電纜絕緣劣化之診斷技術的發展上，應具參考價值。

Electric facilities often experience insulation degradation and hence operation breakdown, causing undesired electric power outage, due to the influence of electric stress, temperature, chemical reaction, mechanical stress and environment by the electrical insulating material. Hence, how to reach the goal of preventive facility-breakdown diagnosis is always a vital research subject.
This thesis selects cable terminator with artificial insulation defect to proceed high-voltage degradation experiments and to measure their partial discharge (PD) signals. Then, skewness, kurtosis and cross correlation factor methods are utilized to extract the features of said underground cable insulation degradation. These features are properly combined and taken as the input signals for a artificial neural network to evaluate the insulation-degradation recognition effect.
The result shows that the recognition successful rate reaches approximately 92% when using the combination of skewness and kurtosis combination as the input signal. It is obvious that this proposed combination does possess a fine degradation recognition effect, the study are likely of value to the development of diagnosis technique for insulation degradation of underground cable.

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