馬達轉動設備在現代工業化中佔有一席之地，不管是工廠的原物料輸送、產線的機械設備驅動或是電動車的驅動系統，皆存在著馬達轉動設備。為了預防轉動設備故障所導致的經濟損失以及安全危害，可透過線上訊號擷取分析對設備進行線上異常偵測與診斷。而傳統的訊號分析透過加速規與比流計即可進行初步的故障檢知，然而訊號式分析在設備初期異常、負載不足及環境干擾下的狀況當中仍易失效誤判。因此，本研究提出以模型式電壓電流訊號分析，進行永磁同步馬達傳動系統之異常偵測與診斷，並針對常見三種傳動元件進行故障檢知分析。結合機械傳動系統與永磁同步馬達物理模型，在永磁同步馬達負載變動不大的狀況下，合理假設系統處於準靜態，並透過系統鑑別找出物理模型及參數。本研究分別應用灰盒模型之遞迴最小平方法以及黑盒模型的子空間數值系統鑑別方法比較，建立系統輸入與輸出的模型關係，驗證模型擬合誤差於合理範圍之內。將實驗訊號應用於模型當中，推估馬達轉動系統之電流輸出，建立電流殘差頻譜基線，觀察異常訊號之殘差頻譜與參數叢集，透過信賴區間檢定判斷出設備的狀況。

Rotating machine is important in our daily life, such as applications in the industrial convey belt, automated manufacturing or electric vehicle. Most of them are driven by electrical motors. In order to prevent the economic losses and safety hazards caused by the unexpected failure of the rotating machine, Fault Detection and Diagnosis (FDD) is a critical function to be implemented for these machines. Moreover, FDD is much easier to be accomplished with modern hardware and internet structure such as AIOT and edge computing. The preliminary FDD employs the traditional signal analysis method using motor running data collected from accelerometers, current transformers, etc.. However, it is challenging to detect incipient faults or developing faults under low motor loading using signal analysis FDD. In addition, the noise in the collected raw signal makes signal analysis even more difficult and costly. To alleviate these disadvantages, a Recursive Least Square (RLS) model based approach is employed for the Permanent Magnet Synchronous Motor (PMSM) transmission fault detection in this research. Three different types of transmission mechanism are investigated for healthy condition and different fault level conditions. An equivalent 2nd order mechanical model is augmented in the PMSM model to depict mechanical transmission and load for the purpose of the model based FDD. The motor-transmission-load system is considered in quasi steady state in this research since the load is just a constant resistor. The parameters in the physical model are identified using RLS and the result compared with a black box approach using Numerical algorithm for Subspace Identification (N4SID). The model predicted motor current is compared with measured current to generate current residual. The experiment data for healthy condition is used to construct current residual spectrum baseline and parameter clustering. Together, the current residual spectrum baseline and the parameter clustering range are used to detect PMSM transmission faults.

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