Enhancing unsupervised fault diagnosis method for rolling bearings using domain adaptive transfer coding techniques

Abstract Intelligent fault diagnosis of rolling bearings in unsupervised conditions remains a great challenge. Transfer learning plays a crucial role in addressing this issue by leveraging knowledge gained from labeled source domain data to enhance the diagnostic performance on unlabeled target domain samples. However, traditional transfer diagnosis techniques often face significant challenges due to missing labels in target domain data under variable operating conditions and considerable discrepancies regarding the distribution of data between target and source domain. These issues can substantially degrade the performance of transfer diagnosis techniques. To address these issues, this study introduces a domain adaptive-based attention group convolution transfer network (DA-AGCTN) specifically for rolling bearing fault diagnosis using unlabeled samples in diverse operational scenarios. The proposed approach consists of two main components, namely DA label generation module (DALGM) and AGCTN. DALGM utilizes a synergistic enhanced stacked autoencoder (SAE) to enhance pseudo label generation through robust feature extraction from unlabeled target domain data. This optimization, which includes a novel domain confusion metric that combines reconstruction loss, domain distinction loss and probabilistic classification loss, is designed to extract domain-invariant features more effectively. Following feature extraction by SAE and dimensionality reduction via t -distribution stochastic neighborhood embedding (abbreviated to t -SNE), the K -means method is employed for clustering, with a subsequent approach for aligning labels to create more accurate pseudo labels. AGCTN integrates attention mechanism and group convolution to capture shared features efficiently, leveraging a pretrained source domain model to enhance target domain generalization. The effectiveness of the proposed DA-AGCTN is corroborated by rolling bearing fault simulation experiments, demonstrating superior diagnostic accuracy and feature distinctiveness compared to existing techniques.