A clustering multi-stage training transfer learning framework for cross simulation domain and experimental domain fault diagnosis

Abstract

Deep learning methods have demonstrated remarkable achievements in the field of fault diagnosis for rotating machinery. However, their effectiveness heavily relies on high-quality labeled samples, which presents a significant challenge owing to the limited availability of such data in engineering applications. To address this realistic issue, we propose a novel simulation-driven transfer learning model called the clustering multi-stage training transfer learning framework (CMSTL) for fault diagnosis of rolling bearings. The fundamental concept of the proposed method is to utilize simulation data as a substitute for labeled actual device data and integrate the suggested clustering learning and multistage training strategies to extract domain-independent and fault-discriminative features from simulation and experimental domains. Specifically, the clustering learning strategy is embedded into the CMSTL model to encourage the feature extractor to acquire distinguishable features associated with different categories while eliminating domain-specific knowledge, which enables samples near the classification decision boundary to cluster towards their respective clustering centers. Additionally, the proposed multistage learning strategy leverages the model trained with a certain level of accuracy in the first stage to annotate actual device samples, thereby enhancing both the precision of the pseudo-label for real data and the overall training stability of the model. The effectiveness and superiority of the proposed method were validated using both artificially damaged and run-to-failure datasets. The comparative analysis results demonstrate that the CMSTL method exhibits a minimum 2.2% improvement in fault diagnosis accuracy and enhances the clustering capability compared to other advanced transfer learning fault diagnosis methods.