Rescaled-LSTM for Predicting Aircraft Component Replacement Under Imbalanced Dataset Constraint

Abstract

Deep learning approaches are continuously achieving state-of-the-art performance in aerospace predictive maintenance modelling. However, the data imbalance distribution issue is still a challenge. It causes performance degradation in predictive models, resulting in unreliable prognostics which prevents predictive models from being widely deployed in realtime aircraft systems. The imbalanced classification problem arises when the distribution of the classes present in the datasets is not uniform, such that the total number of instances in a class is significantly lower than those belonging to the other classes. It becomes more challenging when the imbalance ratio is extreme. This paper proposes a deep learning approach using rescaled Long Short Term Memory (LSTM) modelling for predicting aircraft component replacement under imbalanced dataset constraint. The new approach modifies prediction of each class using rescale weighted cross-entropy loss, which controls the weight of majority classes to have a less contribution to the total loss. The method effectively discounts the effect of misclassification in the imbalanced dataset. It also trains the neural networks faster, reduces over-fitting and makes a better prediction. The results show that the proposed approach is feasible and efficient, achieving high performance and robustness via skewed aircraft central maintenance datasets.