永磁同步馬達具有高效率、高功率密度等優點，在現代工業所追求的自動化系統中扮演重要的角色。匝間短路及退磁是常見且具有破壞性的馬達故障，若在故障初期無法及時偵測，嚴重時將使系統設備產生重大損失，因此預防性診斷的重要性不言可喻。本論文以ANSYS Maxwell 等效電路提取功能提取馬達等效模型，將健康及故障馬達模型結合磁場導向控制法，分析定子電流在不同狀態下的特徵資訊，並以定子電流作為故障診斷之依據。而在故障診斷方法上，以具有良好圖片辨識能力之卷積神經網路建立故障診斷模型。再利用原有馬達之定子與轉子進行故障點加工，透過實測收集定子電流數據，將一維訊號轉成三維圖片數據，以預防性診斷為主要目的訓練故障診斷模型。最後，經實測驗證，即使是輕微故障，亦可達到95%以上之辨識準確率。

The advantages of high efficiency and high power density of permanent magnet synchronous motors (PMSM) are critical in the automation system pursued by modern industry. Inter-turn short-circuit fault (ITSCF) and demagnetization fault (DF) are common and destructive motor faults. If the fault cannot be detected in time at the beginning of the fault, it will cause significant losses to the system equipment. Therefore, the importance of preventive diagnosis is self-evident. This thesis uses ANSYS Maxwell equivalent circuit extraction (ECE) function to extract the motor equivalent model, then combines the healthy and faulty motor models with the magnetic field-oriented control method to analyze the characteristic information of the stator current in different states, and uses the stator current as the basis for fault diagnosis. In the fault diagnosis method, the fault diagnosis method established through the convolutional neural network (CNN) with excellent image recognition ability and then the stator and rotor of the original motor are used to process the fault points, collect stator current signals through experiments, convert the signals into three-dimensional (3-D) images, and train the fault diagnosis model for the main purpose of preventive diagnosis. Finally, it has been verified by experiments that even if a minor fault occurs, the identification accuracy rate reaching more than 95% can be achieved.

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