隨著科技的發展，永磁同步馬達在許多工業應用上愈來愈廣泛。因其有諸多優點，如高效率、高輸出體積比、高功率重量比、構造簡單等。隨著永久磁性材料的成本降低，永磁同步馬達被廣泛應用在許多領域，為了提升安全可靠，維持系統的穩定性，減少意外和損失，故障監診是件很重要的事。本研究使用模型式分析方法進行永磁同步馬達系統之故障診斷，其概念為利用系統的輸入及輸出建立模型，並將模型輸出與真實輸出做比較，用以判別系統狀態。本研究使用粒子群最佳化方法，並將其與常見的傳統遞迴最小平方法作比較，發現在實際使用上粒子群最佳化方法有較好的雜訊強健性之優點，因此將其應用在真實的永磁式同步馬達估測中。首先，建立了健康模型並拿與其他健康資料驗證其擬合性。之後再與故障的真實馬達作比較，比較的方式有觀察其參數的差異及擬合誤差，用以了解設備狀態及分辨機械與電機損壞。輸出殘差的誤差指標可觀察其與健康或損壞模型的差別及藉由抑制主頻影響，凸顯損壞特徵，觀察其損壞類別的殘差頻域圖。最後建立健康馬達的參數區間及殘差頻域特徵峰值範圍，分別使用健康資料與損壞資料來驗證其辨別健康與損壞之效能。

With the improvement of technology, Permanent Magnet Synchronous Motors (PMSM) are beneficial and attractive for many industrial applications, because PMSM has excellent advantages such as high efficiency, high output to volume ratio, high power weight ratio and simple construction. With the reducing cost of the permanent magnet material, PMSM is widely applied in industry and many fields. In order to uphold the reliability and safety, to maintain the system stability, and to reduce the accidents and associated financial loss, it’s important to develop practical on-line method to detect and diagnose the developing fault of PMSM. In this study, a model-based approach is selected as the method for the fault detection. The scheme is to create a model through the input and output data of the real system, and then compare the output of the real system and simulated model and the model parameters identified. Science PSO method is rarely used in PMSM modeling in the reviewed literature, Particle Swarm Optimization(PSO) is chosen as the primery method for the model parameter identification and the result is compared with the result using the traditional Recursive Least Squares(RLS) method. It is observed that the PSO method is more robust in nosie than RLS in simulation and hence PSO is then used in the real PMSM system for model parameter estimation. In this study, a healthy model is created and its fitting result is verified with additional health data. Then the data from a faulty motor is also used to construct a model and compared with this healthy model. The change of the parameters is used to isolate the mechanical and electrical faults. The model error between the healthy and faults system is used as a fault error feature. Residual current is then adopted to from a residual current spectrum that eliminates the dominant supply frequency and enhance the signals indicating faults in the spectrum. Finally, the health range of parameters and associater fault feature magnitude of the residual spectrum are defined for on-line condition monitoring purpose.

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