Deep-Learning-Based Multicondition Transfer Diagnosis of Compound Faults in Electrohydrostatic Actuator

Electrohydrostatic Actuator (EHA) systems operating under variable conditions suffer from data distribution shifts and feature overlapping of compound faults, leading to degraded diagnostic performance. Existing methods often struggle to align features across different working domains and fail to accurately distinguish coupled fault patterns due to signal entanglement. To address these challenges, this paper proposes a Multi-Condition Feature Aligned Capsule Network (MCFACN) for cross-domain compound fault diagnosis. An Adaptive Integrated Maximum Mean Square Discrepancy (AIMMSD) metric, incorporating higher-order statistics and mean constraints, is proposed to capture distribution differences more comprehensively, Furthermore, an aggregated attention routing mechanism is employed to integrate multi-condition source-domain data with convolutional and capsule features, enhancing domain adaptation and feature separability. Extensive experiments on multi-condition transfer tasks demonstrate the superiority of the proposed method. Specifically, MCFACN achieves an average diagnostic accuracy of 99.4%, validating its effectiveness and robustness for reliable aerospace applications.