Fault prediction of aircraft engine based on adaptive hybrid sampling and BiLSTM

To address the class imbalance problem in aero-engine fault prediction, we propose a novel framework integrating adaptive hybrid sampling and bidirectional LSTM (BiLSTM). First, a k-means-based adaptive sampling strategy is proposed that dynamically balances datasets by oversampling minority-class boundaries and undersampling redundant majority clusters. Second, a fault prediction model utilizing BiLSTM is built for fault prediction, which can effectively capture bidirectional temporal dependencies. Experiments on real-world sensor data demonstrate that this approach effectively improves the identification of fault samples in imbalanced datasets.