Data-driven system health monitoring technique using autoencoder for the safety management of commercial aircraft

This paper presents the development of a real-time and data-driven aircraft health monitoring technique to monitor in-flight operations by detecting off-nominal flight operation such as aircraft upset, which is strongly related to aviation safety and is a primary contributor to fatal accidents worldwide. A deep autoencoder is adopted to effectively estimate the complex flight responses from historical flight datasets and capture potential upset precursors. Key sensing variables highly relevant to upset primary factors are selected based on aircraft accident reports. The variables are then preprocessed using decimation and Savitzky–Golay filter for sampling frequency synchronization and denoising. Statistical detection baselines are employed to extract statistically unusual operation patterns that highly correlate with upset precursors. The performance of the developed methodology is evaluated by demonstrating real-time detection of upset precursors in flight datasets. An actual accident scenario is introduced to validate detection robustness. The results show that the proposed aircraft upset detector technique can enhance pilots’ situational awareness by providing early safety alert for more effective flight safety management.