本論文主旨在於利用一套診斷系統來辨識出三相感應電機故障，此方法首先利用加速度計接收振動訊號，再透過快速傅立葉轉換將原始振動訊號從時域轉為頻域信號，取出其特徵後再利用主成份分析法，將多種特徵做適當維度選擇，最後使用倒傳遞類神經法將其分類。本實驗收集四種不同狀態下的轉子斷條訊號，所有資料分為兩組用來訓練及測試倒傳遞類神經法，以分類無故障及三種故障型態。實驗及方法被用於一15瓩三相感應馬達上，實驗結果證實X,Y,Z軸辨識率皆可達93%以上，可應用在轉子斷條故障診斷。

In this thesis, a fault diagnosis system is proposed to perform rotor bar fault detection in three phase induction motor. Vibration signal is acquired by accelerometer and transferred from time domain to frequency domain through fast fourier transform (FFT). A variety of features are selected as appropriate dimensions by using principal component analysis (PCA). The experiment collects rotor bar fault signals under four different conditions. All data are divided into two groups for training and testing back propagation neural network (BPN) to distinguish healthy rotor bar from different fault conditions. The method is applied to a 15 kW three phase induction motor under four conditions of different rotor bar. Experimental results show that the X, Y, and Z axis recognition rates can reach more than 93% and could be applied to fault diagnosis of rotor bar faults.

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