因石油的日漸枯竭及節能減碳的實際需要，以馬達來推進取代傳統引擎，已漸成為未來趨勢。而感應馬達因具有構造簡單、反應快速、推進力量大等優勢較常被使用，並促使線上即時馬達故障監控亦受到重視。然而，故障監控的準確性與即時性是此項工作中較為困難的，且大量統計顯示馬達故障主要發生於定子、軸承與轉子相關處，這些故障又肇因於電力與機械兩部分，其中又有80%的機械相關故障會導致氣隙偏心現象，而降低馬達功率因數與發生振動，進而損耗電能並擴大故障範圍，情況嚴重時，甚至衍生定子與轉子磨擦問題。若能預知感應馬達將發生故障之時間，進而可先期排訂保養或更換即將故障零組件之時程，以避免因馬達的突然故障而造成人員受傷或營運上的損失。
本文應用Matlab/Simulink來模擬比較感應馬達於正常運轉、配置不均與轉子偏心情況之電流訊號差異，並模擬感應馬達氣隙偏心的故障特徵驗證，再與相關文獻數據比對，最後藉由模糊運算實現不需使用傳統方法，先轉到頻域，再用神經元演算法分析故障狀況，而能於時域下快速且準確比對出感應馬達的偏心故障訊息，並予以判定嚴重等級。

Owing to the gradual depletion of oil and a concern for environmental protection, motors have become the drive system of the future instead of the combustion engine. Among various types of motors, induction motors are often used due to their simple structure, fast reaction, and powerful propulsion force. Thus, the safe operation of motor vehicles is a major concern with the trends in the online fault monitor. However, the accuracy of online monitoring is the most difficult task. Based on statistics, the motor failures caused by electric and mechanical parts may occur mainly in stator, bearing and rotor. Among mechanical faults, 80% of relevant troubles may cause the phenomenon of air-gap eccentricity, leading to the power factor decreasing, motor vibrations, power losses and enlargement of the fault area. Particularly, it may result in the rotor and stator sides to chaft each other in a serious situation. If one can detect motor faults in advance, maintenance schedule or parts replacement can be properly arranged to reduce the risk of suddenly shut down in motor operation.
This thesis simulates the differences of the characteristics of electric current signals for a three-phase induction motor under normal condition, uneven dispose fault and air-gap eccentric fault using Matlab/Simulink. To verify the feasibility of the models developed in the MATLAB/Simulink environment, the simulated fault signals are compared with the results obtained by other research. Finally, this thesis shows that the air-gap eccentricity degree can rapidly and reliably be diagnosed without using the traditional method to transform the fault signals into the frequency domain by analyzing them with a neural algorithm.

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