Jul 2026· Advances in Engineering Technology Research· Vol 17, pp. 329· 0 citations
TL;DR
A transferability-aware weighted fusion strategy integrates multi-source classification results and a shared feature extraction network integrating multi-scale convolutions, LSTM, and ECA mechanism is constructed to extract domain-invariant features.
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.
Experimental validation on the Harbin Institute of Technology aero-engine inter-shaft bearing dataset shows that the proposed model achieves 97% diagnostic accuracy under extreme noise conditions (SNR = -5 dB).
Yang Wang, Boliang Zhang· Scientific Reports· 0 citations
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