Interpretable Fault Diagnosis for Liquid Rocket Engines via Component-wise MLP-Based Granger Causality Feature Extraction

Liquid rocket engine (LRE) fault diagnosis is critical for successful space launch missions, enabling timely avoidance of safety hazards, while accurate post-failure analysis prevents subsequent economic losses. However, the complexity of LRE systems and the "black-box" nature of current deep learning-based diagnostic methods hinder interpretable fault diagnosis. This paper establishes Granger causality (GC) extraction-based component-wise multi-layer perceptron (GCMLP), achieving high fault diagnosis accuracy while leveraging GC to enhance diagnostic interpretability. First, component-wise MLP networks are constructed for distinct LRE variables to extract inter-variable GC relationships. Second, dedicated predictors are designed for each variable, leveraging historical data and GC relationships to forecast future states, thereby ensuring GC reliability. Finally, the extracted GC features are utilized for fault classification, guaranteeing feature discriminability and diagnosis accuracy. This study simulates 6 critical fault modes in LRE using Simulink. Based on the generated simulation data, GCMLP demonstrates superior fault localization accuracy compared to benchmark methods, validating its efficacy and robustness.