在當今工業中感應電動機的應用非常廣泛，若無法提前發現感應電動機的缺陷，突然的故障將會產生龐大損失以及後續問題。並且隨著工業4.0的發展以及物聯網技術的發展，智慧工廠若採用雲端運算(Cloud Computing)傳送大量原始資料將會導致頻寬壅塞等狀況。因此，本文開發一套結合量測振動以及定子電流的馬達故障檢測系統，並採用基於智慧物聯網下的邊緣運算架構。在本文的系統架構中，振動以及電流訊號由不同的感測器測量，並在智慧感測模組中的微控制器進行資料預處理，經由智慧感測模組預處理後的特徵，再透過邊緣運算平台進行資料融合並透過預先訓練之機器學習模型進行感應電動機的缺陷分類，使用者之裝置即可透過網路即時監控馬達狀態。此外，本文依照鼠籠式感應電動機之常見的缺陷種類，人為製作多顆之缺陷馬達作為實驗平台，設計實驗針對常見缺陷種類馬達使用智慧感測模組進行分析，並使用邊緣運算平台診斷馬達缺陷種類，以此進行系統驗證，資料融合後馬達故障辨識結果有100 %的準確率，且上傳的資料量大小相比於傳統的雲端運算少了40倍。此邊緣運算系統通過資料融合能有效提高辨識之準確度，並能有效降低上傳之資料量。

The application of induction motors in industries is highly extensive. Failure to detect motor defects in advance can lead to significant losses and subsequent issues. With the development of Industry 4.0 and IoT technologies, the large volume of raw data transmissions through cloud computing can result in bandwidth congestion and other challenges in smart factories. Therefore, this study develops a motor fault detection system that combines vibration and current sensing and utilizes an edge computing architecture based on the concept of Internet of Things (IoT). In the proposed system architecture, vibration and current signals are measured by different sensors, and feature extraction and processing are performed by a microcontroller in the smart sensing module. The processed features are then fused through the edge computing platform, and a pre-trained machine learning model is employed for motor defect classification. Users can monitor the motor status in real-time through their devices via the network. Furthermore, in this study, multiple defective motors were artificially created based on common types of faults in cage induction motors. Experiments were designed to analyze these motors using the smart sensing module and diagnose the types of motor defects using the edge computing platform. This was done to validate the system's performance. The results of motor fault identification after data fusion achieved 100% accuracy, and the amount of data uploaded is 40 times smaller compared to traditional cloud computing methods. This edge computing system can effectively reduce the amount of data uploaded and improve recognition accuracy through data fusion.

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