A fault diagnosis method based on interpretable machine learning model and decision visualization for HVs

Abstract

High-speed and highly dynamic hypersonic vehicles demand exceptional safety and reliability during flight. Accurate detection and localization of faults in actuators and reaction control systems are pivotal for controlling and predicting operational states. However, this process encounters challenges such as multiple fault modes, limited data availability, and suboptimal diagnostic accuracy. Our focus is on common fault types in reaction control systems and actuators. We have designed a residual module and an attention module to construct an interpretable fault diagnosis model that extracts deep features from fault residual sequences and state parameter sequences. This model allows for the simultaneous and precise identification of fault type, location, and occurrence time. Furthermore, we visualize the diagnosis process through the use of attention weights and class activation mapping, thereby enhancing the interpretability of the fault diagnosis and bolstering the reliability of the results. Our findings reveal that both the residual module and attention module enhance diagnostic accuracy. In the diagnosis network, shallow attention primarily facilitates feature fusion, whereas deep attention primarily serves to filter features and improve detection capabilities. Without increasing computational complexity, the interpretable fault diagnosis model achieved an accuracy of 96.65%, and the fault time localization error was reduced by 86.15%. The proposed method simplifies model training and elevates fault detection accuracy, offering a reliable approach for isolating and identifying actuator faults.