Fault Diagnosis of Aircraft Landing Gear Retraction Mechanism Based on Fusion of T-S Fault Tree Mechanism

Current fault diagnosis methods for aircraft landing gear retraction mechanisms at the mechanical system level primarily rely on static logical analyses, such as fault tree analysis, which are difficult to effectively integrate with data-driven diagnostic models. This limitation leads to a disconnect between preliminary analytical results and subsequent diagnostic model applications. To address this issue, this paper proposes a fault diagnosis model that integrates T-S fault tree knowledge with an attention mechanism. The proposed model adopts a physically interpretable prior-knowledge integration strategy, thereby establishing a bridge between fault tree analysis and diagnostic modeling. Specifically, a data-and-mechanism-driven attention module is designed to map the importance analysis results of the T-S fault tree into a mechanism-informed weight vector in the neural network. Through adaptive weighting of sensor signal features, the model is guided to focus on high-risk fault features. In addition, a fault dataset covering typical failure modes of the landing gear retraction mechanism is constructed based on multibody dynamics simulations for validation. Experimental results show that the proposed model effectively incorporates prior mechanism knowledge and achieves higher diagnostic accuracy than conventional deep learning models for typical mechanical faults, including joint wear, insufficient joint lubrication and spring fatigue, thereby demonstrating its feasibility and effectiveness.