Boosting efficient attention assisted cyclic adversarial auto-encoder for rotating component fault diagnosis under low label rates

Abstract

In practical engineering scenarios with limited labeled samples, conventional semi-supervised diagnostic methods face challenges in achieving satisfactory identification outcomes. To address the aforementioned issues, this paper introduces an incremental semi-supervised learning (ISL) approach based on boosting efficient attention (BEA) assisted cyclic adversarial auto-encoder (CAAE), referred to as BEA-CAAE. The CAAE enhances unsupervised feature representation by simultaneously constraining the distribution of encoded features and aligning elements of the reconstructed samples through a cyclic encoding strategy. The BEA improves classical attention weights' activation strength to better capture vital information, thereby boosting the feature extraction capabilities of both CAAE and the classifier. The ISL employs a stepwise pseudo-label propagation strategy to incrementally filter high-confidence samples, enhancing sample and label utilization, and improving diagnostic accuracy under low label rate conditions. Experiments conducted on multiple test rigs with simple structures as well as large-scale rotating components test rigs that mimic real-world engineering conditions have demonstrated that the proposed method exhibits a significant advantage over existing semi-supervised fault diagnosis approaches in terms of fault diagnosis accuracy and generalization capability, especially under low label rate conditions.