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.

Autonomous trajectory planning for electric Vertical Takeoff and Landing (eVTOL) Unmanned Aerial Vehicles (UAVs) faces the dual challenges of low-altitude environmental interference and limited onboard energy, which affects the reliability and safety of unmanned missions. To address these challenges, this paper develops the EA-TD3 autonomous trajectory planning framework for eVTOL UAV systems. First, a stochastic urban wind field model is established to simulate low-altitude interference. Then, by integrating eVTOL UAV battery discharge data from Carnegie Mellon University (CMU), a mapping relationship between maneuvers and energy consumption is identified to construct a nonlinear energy consumption model. Finally, an energy boundary penalty function is introduced into the TD3 algorithm to ensure that trajectory planning remains within battery safety margins. Experiments based on the parameters of the EH216-S platform show that EA-TD3 achieves a near 100.00% success rate under ideal conditions and outperforms benchmark algorithms while reducing average energy consumption by 11.6%. Under an energy constraint of 120 J, its success rate remains at 87.80%, which exceeds the performance of the DDPG, SAC, and standard TD3 algorithms. This study optimizes the autonomous trajectory planning of eVTOL UAV platforms in urban air mobility (UAM) to improve the energy perception and power management of the autonomous system.