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Fault detection (FD) is important for health monitoring and safe operation of dynamical systems. Previous studies use model-based approaches which are sensitive to system specifics, attenuating the robustness. Data-driven methods have claimed accurate performances which scale well to different cases, but the algorithmic structures and enclosed operations are “black,” jeopardizing its robustness. To address these issues, exemplifying the FD problem of aircraft air data sensors, we explore to develop a robust (accurate, scalable, explainable, and interpretable) FD scheme using a typical data-driven method, i.e., deep neural networks (DNN). To guarantee the scalability, aircraft inertial reference unit measurements are adopted as equivalent inputs to the DNN, and a database associated with 6 different aircraft/flight conditions is constructed. Convolutional neural networks (CNN) and long-short time memory (LSTM) blocks are used in the DNN scheme for accurate FD performances. To enhance robustness of the DNN, we also develop two new concepts: “large structure” which corresponds to the parameters that can be objectively optimized (e.g., CNN kernel size) via certain metrics (e.g., accuracy) and “small structure” that conveys subjective understanding of humans (e.g., class activation mapping in CNN) within a certain context (e.g., object detection). We illustrate the optimization process we adopted in devising the DNN large structure, which yields accurate (90%) and scalable (24 diverse cases) performances. We also interpret the DNN small structure via class activation mapping, which yields promising results and solidifies the robustness of DNN. Lessons and experiences we learned are also summarized in the paper, which we believe is instructive for addressing the FD problems in other similar fields.

Robust Data-Driven Fault Detection: An Application to Aircraft Air Data Sensors