感應電動機具有價格低廉、堅固耐用的特性，一直以來在工業發展上扮演著很重要的角色，在任何工廠都是不可或缺的動力來源。以往在運維策略上，大部分的工廠都是採取定期性維護保養或是狀態性維護保養。近年來，受益於大數據分析，可利用機器學習之技術發現潛期的故障及早進行維修，將保養方式提升為主動性維護保養。
本論文以人為加工之方式製造四種實驗模型，量測實驗模型之三軸加速度，可從中取得運轉狀態為「正常」和「警戒」的資料，但運轉狀態為「危險」的資料無法藉由實際實驗模型取得，因此本研究選擇將量測到的數據線性放大固定倍數，使其超過振動規範VDI-2056之門檻，來模擬運轉狀態為「危險」的資料。將量測到的三軸加速度經過訊號處理和計算後得到振動參數，通過將這些參數以主成份分析進行篩選，再將篩選過後的參數進行K均值分群法，評估其分群效果。最後，本研究以四種案例進行分析，共有16種分群結果，根據分群結果找出適合用於主動式運轉狀態監測之參數共5個，以振動規範來進行驗證，發現分類的準確率皆為100%。

Inductive motors are inexpensive, rugged and have always played an important role in industrial development. They are indispensable power sources in the factory. In the past, factories often adopt O&M (Operation and maintenance) strategies based on “Time-Based Maintenance” or “Condition-Based Maintenance”. In recent years, benefiting from big data analysis, we can identify the potential faults and fix it early by machine learning. This is the so-called proactive maintenance.
In this thesis, we first manufacture four customer-made experimental models and measure the triaxial signals from these models. We get the data of opearation condition of “Normal” and “Warning” from the afore-mentioned experimental models. In real opereation, we cannot get the data of opearation condition of “Danger”. Alternatively, we linearly amplify the measurement data to simulate the danger opearating condition data, according to the VDI-2056 standrad. We obtain vibration parameters from triaxial signals by signal processing and caculation, and extract feature parameters by principal component analysis (PCA). Then, according to the extracted parameters, classify the operating status by the K-means clustering. Finally, 4 case studies conducted and 16 clustering results were obtained. We find out that there are 5 parameters suitable for proactive condition monitoring. Results obtained from simulations show that the classifying accuracy for different operation conditions is 100%.

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