| Citation: | ZHANG Zhenliang, HE Rongrong, ZHOU Xiangzhi, et al. Condition monitoring scheme of C919 hydraulic system based on CNN-BiLSTM-Triplet[J]. Journal of Aerospace Power, 2026, 41（3）：20240716 doi: 10.13224/j.cnki.jasp.20240716
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To address the challenges posed by complex fault modes in hydraulic systems and the difficulty of status monitoring in domestically manufactured aircraft, convolutional neural networks-bidirectional long short-term memory-triplet triplet attention （CNN-BiLSTM-Triplet） model was proposed for hydraulic system fault identification and feature analysis. Initially, the data were dimensionally reduced and reshaped into a three-dimensional format. A convolutional network was then employed to extract feature maps. Triplet attention was calculated using rotation operations and residual transformations, and subsequently transmitted to the network to enhance specific data features. BiLSTM was employed to capture and learn the extracted features, with a Dropout layer and L2 regularization incorporated to mitigate overfitting and enhance model robustness. The model’s performance and generalization ability were validated through fault datasets of hydraulic systems. Model interpretation method was applied for model feature analysis. Furthermore, the behavior of specific features and misclassified samples was analyzed to determine the status monitoring indicators and their relative priorities. Experimental results demonstrated that the proposed method achieved a recognition rate above 95.77% for all five fault modes in the hydraulic system, and identified pressure and flow as the most significant features for faults. Additionally, based on the specific structure of the C919 aircraft hydraulic system, a pressure pulsation sensor was added to improve the condition monitoring scheme.
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