In this article, we propose a novel approach to intelligent fault diagnosis in complex systems, leveraging domain adaptation and semi-supervised learning techniques. Our method, called Calibrated Adaptive Teacher (CAT), combines the strengths of both supervised and unsupervised learning methods to improve accuracy and robustness in detecting faults across different domains. By adaptively adjusting the level of trust placed on the teacher model based on its past performance, CAT achieves state-of-the-art results in various transfer tasks while maintaining a simple architecture.
Ablation Studies
To evaluate the effectiveness of our proposed method, we conducted ablation studies to analyze the impact of individual components on the overall performance. Our findings show that the adaptive thresholding strategy outperforms a fixed confidence threshold in most transfer tasks, highlighting the importance of adapting to changing conditions in real-world applications.
Conclusion
Our proposed method, Calibrated Adaptive Teacher (CAT), offers a novel approach to domain adaptive intelligent fault diagnosis by combining the strengths of both supervised and unsupervised learning methods. By adaptively adjusting the level of trust placed on the teacher model based on its past performance, CAT achieves state-of-the-art results in various transfer tasks while maintaining a simple architecture. These findings have significant implications for real-world applications where fault diagnosis is crucial for ensuring system reliability and safety.
