Unforeseen technical failures contribute significantly to airline delays, highlighting the need for predictive maintenance. However, developing reliable prognostic models in aviation is challenging due to strict safety requirements, limited labeled data, and the need for interpretable and trustworthy predictions. This research proposes a hybrid framework for remaining useful life (RUL) prediction that integrates multimodal domain knowledge available to airlines, such as sensor data, contextual information and reliability insights, into interpretable and uncertainty-aware algorithms. To this end, the proposed framework resorts to unsupervised degradation extraction with knowledge-informed autoencoders and supports extensions for failure mode segmentation. Initial experiments on a benchmark dataset show promising results, and application to real-world commercial aircraft data is planned to further validate the approach.
Goglio et al. (Sun,) studied this question.