本論文旨在感應馬達重大故障發生前分析出瑕疵，應用隨機森林辨識三相感應馬達之轉子與軸承故障。利用加速規取得振動訊號後，透過快速傅立葉轉換將訊號從時域轉變為頻域。獲得之訊號於特徵建立後利用標準化及主成份分析法處理並當作輸入資料給隨機森林進行辨識。本研究中，製作了轉子及軸承的故障類型，分類器用來辨識轉子及軸承故障之狀況，以一20馬力三相感應馬達為演示。實驗結果中，轉子及軸承故障分析之最佳辨識率與袋外錯誤率分別為100%、0.35%及99.33%、1.00%。高的辨識率不僅優於其他文獻之方法且證明隨機森林分類器用於馬達故障分析之可行性。

In this thesis, the main purpose is to analyze defects in an induction motor before significant faults occur. The rotor faults and the bearing faults of a three-phase induction motor are recognized by using the random forest. The vibration signals are obtained from the accelerometers and transferred from the time domain to the frequency domain via fast Fourier transform. The obtained signals are processed by standardization and principal component analysis after the feature construction and used as input data to the random forest for recognizing. In this study, the types of rotor fault and bearing fault are created. The classifier is used to recognize the types of rotor fault and bearing fault. And, a demonstration on a 20 hp three-phase induction motor is developed. In the experimental results, the best recognition rate and out-of-bag (OOB) error rate of faults analysis of the rotor and the bearing are 100% and 0.35%, 99.33% and 1.00%, respectively. The high recognition rate is superior to the analysis method of the other literature and shows the high feasibility of the random forest classifier in motor faults analysis.

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