領域自適應是機械故障診斷領域的一個關鍵挑戰，因為傳統故障診斷模型的性能在應用於不同的工作條件或域時會顯著降低。本論文中，我們提出了一種用於機械故障診斷領域自適應的鑑別分類器生成對抗網路 (Critic Classifier Generative Adversarial Network GAN)新方法。我們的方法旨在通過對齊源域和目標域來提高診斷性能，從而實現它們之間的高效知識轉移。我們利用 Critic Classifier GAN 框架的強大功能，該框架結合了生成器對抗網路和鑑別分類器，來學習域不變表示並準確分類故障模式。此外，我們採用差異損失函數，如最大平均差異（MMD）和最大分類器差異（MCD）方法，以進一步增強域對齊和分類對齊。與現有領域自適應技術相比，對各種機械故障數據集進行的實驗評估證實了我們提出的方法的有效性和穩健性。 我們的研究有效地解決了域轉移所帶來的挑戰，並在各種工作條件下的機械故障診斷中取得了最佳性能。

Domain adaptation is a crucial challenge in the field of machinery fault diagnosis, as the performance of traditional fault diagnosis models can significantly degrade when applied to different working conditions or domains. In this thesis, we propose a novel approach for domain adaptation in machinery fault diagnosis based on the Critic Classifier Generative Adversarial Network (GAN). Our method aims to improve diagnostic performance by aligning the source and target domains, enabling effective knowledge transfer between them. We leverage the power of the Critic Classifier GAN framework, which incorporates both a generator adversarial network and a critic classifier, to learn domain-invariant representations and classify fault patterns accurately. Additionally, we employ domain discrepancy loss functions, such as Maximum Mean Discrepancy (MMD) and the Maximum Classifier Discrepancy (MCD) method, to further enhance domain alignment and classifiers to align distributions. Experimental evaluations conducted on various mechanical failure datasets confirm the effectiveness and robustness of our proposed method in comparison to existing domain adaptation techniques. Our proposed solution effectively overcomes the challenges arising from domain shift and achieved state-of-the-art performance in machinery fault diagnosis under various working conditions.

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