工業上常用的感應電動機意外故障和停機可能會導致重大事故並導致高額的損失。隨著微機電系統技術和雲端運算的成熟，穩定性佳且成本低的感測器已被大量運用在感應電機狀態監測，然而大量資料的傳輸將會造成嚴重的頻寬壅塞，甚至產生嚴重的延遲問題。邊緣運算的分散式計算架構，將儲存、處理與分析資料之功能，從雲端分散到各個邊緣端裝置，提供了可承受的計算能力、足夠的存儲空間和快速的響應時間。本研究在工業物聯網系統架構下導入邊緣運算與深度學習，以常見故障之三相感應電動機作為實驗平台，開發一即時的監測與診斷感應電動機之邊緣物聯網系統。節點層應用振動智慧感測模組完成時域及頻域特徵之運算；邊緣層作為閘道器進行橫向和縱向的訊息傳輸；雲層分析時域及頻域之特徵，並透過本論文所提出之兩階段特徵選擇演算法篩選出具代表性之特徵，訓練深度學習模型，最後將訓練好的模型卸載至邊緣層，即時做出故障型態之診斷。實驗結果證明本論文開發之馬達故障辨識系統準確率高達98%，與傳統雲端運算之系統相比能有效減少約150倍頻寬，且達到近乎即時的辨識，對於馬達缺陷之診斷提出一成本低且即時之可行方案。

Unexpected failures and shutdowns in induction motors can lead to major accidents and result in significant losses. With the maturity of microelectromechanical systems (MEMS) technology and cloud computing, stable and cost-effective sensors have been widely utilized in the condition monitoring of induction motors. However, the transmission of large amounts of data can cause serious bandwidth congestion and latency issues. Edge computing is a distributed computing architecture that decentralizes the storage, processing, and analysis of data from the cloud to various edge devices, providing sufficient computing power, storage, and fast response times.
This study introduces edge computing and deep learning into the architecture of an industrial internet of things (IoT) system. Using a three-phase induction motors as the experimental platform, an edge IoT system for real-time monitoring and diagnosis of induction motors is developed. The node layer utilizes vibration intelligent sensing modules to compute time-domain and frequency-domain features. The edge layer acts as a gateway for horizontal and vertical message transmission. The cloud layer analyzes time-domain and frequency-domain features and applies the two-stage feature selection algorithm proposed in this paper to select representative features. These selected features are then used to train a deep learning model. Finally, the trained model is deployed to the edge layer for real-time fault diagnosis. The experimental results demonstrate that this motor fault recognition system achieves an accuracy rate of up to 98%, significantly reducing the required bandwidth by approximately 150 times compared to traditional cloud computing systems, and achieving near real-time recognition.

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