GMSIT-WDADA: a deep adversarial domain adaptation framework based on state space model for remaining useful life prediction

Abstract Accurate remaining useful life (RUL) prediction for complex systems like aero-engines is vital for optimizing maintenance strategies. However, the intrinsic complexity of engine degradation, marked by highly coupled sensor dependencies and multi-scale temporal dynamics, continues to pose a fundamental challenge to data-driven approaches. Existing methods often exhibit incomplete temporal feature modeling and fail to capture the intrinsic spatial coupling among multi-channel sensor signals. Moreover, domain shifts caused by fluctuating flight conditions and hybrid fault modes lead to significant performance degradation in unlabeled target domains.To address these challenges, this paper proposes a deep adversarial domain adaptive transfer learning model named gated-Mamba sparse iTransformer (GMSIT)-WDADA.First, we design GMSIT, a feature extractor that incorporates gated convolution, a selective state space model, and a sparse inverted Transformer. This architecture achieves the extraction of multi-scale temporal features, including short-term transient local features, long-term trend global features, and cross-temporal correlations, while explicitly capturing the spatial coupling relationships across sensors. Second, a neural network is employed to estimate the Wasserstein distance as a replacement for the conventional domain discriminator, achieving more stable cross-domain feature alignment. Experimental results on the C-MAPSS dataset demonstrate that the proposed model can accurately predict RUL values. Furthermore, RUL prediction experiments on 12 cross-domain scenarios of turbofan engines demonstrate that GMSIT-WDADA performs reliable RUL prediction in unlabeled target domains, achieving 7% lower average RMSE and 39% lower average Score compared with the best methods, further verifying the superiority and robustness of the proposed method.