隨著科技進步，馬達設備已廣泛運用在各領域中，然而現今工廠維護策略大多採取定期性保養方式，雖然可以降低機台發生嚴重故障，但仍無法即時發現馬達潛在的劣化資訊，且需耗費較多的人力與成本。因此若能在馬達發生故障初期，先找出故障徵兆，就能盡早安排維修策略，減少馬達發生故障時所造成的意外或投入過多的維修成本，達到預知保養的效果。
本研究旨在運用電氣訊號於感應馬達進行運轉狀態監測與故障診斷之研究。首先，量測健康馬達三相電壓與電流訊號並計算國際規範中相關電氣指標，接著透過傅立葉分析將電流時域訊號轉換成頻域訊號，取得健康馬達之電氣指標及頻譜特徵作為背景值；其次，運用健康馬達歷史數據建立門檻值且設計一套基於模糊演算法之運轉狀態系統，評估馬達目前的運轉狀態，當馬達運轉狀態發生異常時，將會啟動故障診斷系統判別馬達故障類別；最後，以人機介面顯示相關電氣指標及其分析之結果。本研究以四種不同馬達瑕疵模型經由實驗案例分析後驗證系統之可行性。

With the progress of science and technology, motors have been widely used in various fields. About the maintenance of motors, most factories adopt the strategy of regular checking. Although it can reduce the occurrence of critical failure, it still can't find the potential deterioration of motor immediately. It also consumes lots of labor force and expense. Therefore, if the fault symptom can be detected in advance, the maintenance personnel can arrange some inspection as soon as possible. There are many benefits including to reduce the influence when motors break down, avoid the excessive costs and even achieve the effect of predictive maintenance.

This study aims to propose a practical methodology about the operation condition monitoring and fault diagnosis of induction motor. It can be divided into three parts. In the first part, it needs to measure the three-phase voltage and current signals of health motor. Then, the relevant electrical indexes in the international standards should be calculated. In order to obtain the electrical indexes and spectrum characteristics of the healthy motor which are as the background value, this methodology converts the current signals from time-domain to frequency domain by Fourier analysis. The second part uses historical data of healthy motor to establish the threshold values and design an operation state system based on fuzzy algorithm. Its work is to evaluate the current operation status of the motor. When the operation status of the motor is abnormal, it will start the fault diagnosis to identify the fault types. Finally, the third part is to display the relevant electrical indicators and the analysis results on the human machine interface. In this study, four different defect models of motors are used to verify the probability of the proposed methodology.

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