Fault Diagnosis of Propulsion Shaft Bearings Based on Deep Multi-Scale Multi-Source Subdomain Adaptation

Abstract

To improve the fault diagnosis accuracy of marine propulsion shaft bearings under variable working conditions and with limited samples, a Multi-scale Deep Multi-source Subdomain Adaptation Network (MDMSAN) is proposed. First, DCGAN is employed to augment source domain samples. A shared feature extraction network integrating multi-scale convolutions, LSTM, and ECA mechanism is constructed to extract domain-invariant features. Private feature extractors are designed for each source-target domain pair, utilizing LMMD for fine-grained subdomain alignment. A transferability-aware weighted fusion strategy integrates multi-source classification results. Experiments on CWRU and PT500 datasets demonstrate that MDMSAN achieves average accuracies of 99.92% and 99.73% under variable working conditions, and maintains 98.85% accuracy with only 1/16 of the target samples, significantly outperforming existing methods.