為了保持工業設備的可靠性和效率，馬達故障診斷至關重要，這涉及製程的成本和安全運作。而在全球持續推動高效能馬達以節能減碳的趨勢，永磁輔助磁阻馬達具有效率高、扭矩密度高、成本效益的優勢，因此發展愈趨受到重視。現今，深度學習技術於馬達故障診斷領域展現出可自動提取特徵與優秀的分類性能，然而模型可能在面對與訓練資料分佈不同的新情境時表現不佳，其泛化能力仍然是一個挑戰，對於實際落地應用仍有侷限性。
為了解決這些問題，本研究鑑別馬達模型的電氣與機械參數，建立系統輸入與輸出的模型，並提取馬達的物理狀態，提出了一種創新的混合深度學習架構。其整合一維卷積神經網路、長短期記憶、人工神經網路三個獨立通道，可自動提取時域及頻率域的電訊號，以及物理特徵中空間特徵與故障類別的依賴關係。這個架構可辨別健康馬達、傳動齒輪損壞、傳動齒輪潤滑油缺失與繞組匝間短路四種狀態，該架構結合傳統模型驅動方法的優點和資料驅動方法的靈活性，不僅對與訓練資料相同負載下的資料診斷具有高準確性和可靠性，也能夠有效辨別與訓練階段不同負載的獨立測試資料，這展現了模型良好的泛化能力與競爭力。
研究的潛在應用包括在工業自動化和維護預測中，實現更高效和成本效益的故障診斷解決方案。此研究未來將有機會擴展到其他類型的電機系統。

To maintain the reliability and efficiency of industrial equipment, motor fault diagnosis is crucial as it involves process costs and safe operations. In the global trend of promoting high-performance motors to save energy and reduce carbon emissions, permanent magnet-assisted synchronous reluctance motors are gaining more attention due to their high efficiency, high torque density, and cost-effectiveness. Deep learning techniques show promising automatic feature extraction and excellent classification performance in motor fault diagnosis. However, models may perform poorly when faced with new situations different from the training data distribution, and their generalization ability remains a challenge, posing limitations for practical applications.
To address these issues, this study identifies parameters of motor models, establishes a model of system inputs and outputs, and extracts the physical states of the motor, proposing an innovative hybrid deep learning architecture. This architecture integrates three independent channels: one-dimensional convolutional neural networks, long short-term memory networks, and artificial neural networks. It can automatically extract electrical signals in the time and frequency domains, the spatial characteristics of physical features, and the dependencies of fault categories. This architecture can distinguish four states: healthy motor, damaged drive gear, lack of lubrication in drive gear, and inter-turn short circuit in windings. It combines the advantages of traditional model-driven methods with the flexibility of data-driven approaches, providing high accuracy and reliability in diagnosing data under the same load as the training data and effectively identifying independent test data with different loads, demonstrating good generalization ability and competitiveness.
The potential applications of this research are significant. It could lead to more efficient and cost-effective fault diagnosis solutions in industrial automation and predictive maintenance. Moreover, the findings of this study may have broader implications, extending to other types of motor systems, thereby contributing to the advancement of the field.

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