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• This paper innovatively designs a diagnosis framework for aero-pipelines with a lightweight spatial-temporal model. • The lightweight spatial feature extraction module is designed to extract fine-grained features from fault data. • The bi-directional temporal feature extraction module is designed to extract coarse-grained features from fault data. • A multi-channel lightweight diagnosis model has been designed from the requirement of a lightweight computing process. • A novel intelligent model for end-to-end aero-engine pipeline system composite fault diagnosis. Accurate identification of faults in the aero-engine hydraulic pipelines is of great significance for condition monitoring of engines as well as safe and reliable operation of aircraft. However, due to the complexity of the structure of the aero-engine pipeline systems, the correlation of the coupling, and the multi-source nature of the excitation it is subjected to, the identification of failure characteristics has been an urgent problem in both engineering and academia. In addition, so there is no more qualitative formula or characteristic index to describe the fault of the aero-hydraulic pipeline. Therefore, to overcome these challenges, in this paper, a new compound faults diagnosis framework, namely Lightweight Spatial-Temporal Model Fusion Self-Attention Mechanism (LST-SATM-Net), is proposed for the aero-engine hydraulic pipeline systems. Firstly, the lightweight spatial feature extraction module is designed to extract fine-grained features from aero-engine hydraulic pipeline fault data and the bi-directional temporal feature extraction module is designed to extract coarse-grained features with temporal patterns from fine-grained features, achieving coarse and fine-grained feature fusion. Then, a self-attention mechanism is integrated into the spatial–temporal model for optimization, enabling the model to automatically learn important features from data with multiple sources of coupled vibration excitation, enhancing the performance of critical features from the correlated coupling of compound faults. The powerful noise interference is suppressed and the weighting coefficients are continuously updated to make the final decision more focused. Consequently, the recognition accuracy of the LST-SATM-Net model is improved and the feature learning of the neural network model is more flexible. The results demonstrated that compared with seven other advanced methods, the proposed LST-SATM-Net model is capable of diagnosing compound faults in the hydraulic pipeline system of aero-engine more accurately after being trained by experimental measurement data. This impressive finding indicates that the proposed LST-SATM-Net model can be applied to the actual condition monitoring of aero-engine hydraulic pipelines to reduce maintenance costs and downtime. [ABSTRACT FROM AUTHOR]