LAUSR

The electro-hydrostatic actuator (EHA), known for its advantages such as minimal throttling loss, high efficiency, and a significant volume-to-power ratio, has found extensive application in the fields of aeronautics and astronautics. However, ensuring the safety of aircraft that utilize EHAs requires efficient fault diagnosis due to the demanding operational conditions and prolonged usage. Traditional diagnostic approaches face challenges such as intricate fault modeling, complex multi-channel monitoring data, and a limited number of fault samples within the electro-hydraulic system. To overcome these challenges, we propose an intelligent diagnosis method based on a multi-source information convolutional residual network. Specifically, a multis-cale kernel is implemented to capture features at different scales, enhancing model expressiveness. The efficiency channel attention mechanism dynamically focuses on relevant channel features to improve feature learning ability. The residual network adaptively recalibrates features at each layer to facilitate fault feature learning. Additionally, the activate or not activation function is introduced to selectively activate shallow features, thereby improving the feature representation and generalization capability of the model. Experimental data from the EHA system validates the superiority of the proposed method, demonstrating a significant enhancement in the diagnostic accuracy of EHAs with limited samples.