Novel Sparse Auto-encoder Framework with Pseudo-labeled Reinforcement for Cross Domain Fault Diagnosis with Imbalanced Samples

Abstract By applying diagnostic expertise from one area to another that is closely related, transfer fault diagnosis is an efficient strategy for guaranteeing the secure and dependable operation of mechanical equipment. However, the majority of rotating machinery monitoring data are gathered in industrial applications under normal operating settings, which leads to an imbalance between positive and negative samples. Consequently, performing high-precision fault diagnosis with imbalanced data and different working conditions becomes challenging due to the escalating difficulty and cost of acquiring labeled fault samples. For cross domain fault diagnosis, an enhanced deep transfer Sparse Auto-Encoder (SAE) framework is provided. This approach leverages a SAE to delve deeper into fault features within imbalanced data and reconstruct the samples accordingly. Furthermore, the research proposes a Feature Domain Penalty Term (FDPT) to facilitate cross-domain training by aligning the distribution of the source and target domain data which can reduce data distribution deviation. A Pseudo-Labeled Reinforcement Training (PLRT) method is presented to further improve cross-domain classification accuracy with imbalanced samples.
Extensive experiments were conducted on two datasets to assess the proposed method. The results were compared with other algorithms, demonstrating the effectiveness and superiority of the proposed approach.