Fault prediction of aircraft engine based on adaptive hybrid sampling and BiLSTM
Abstract 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 minorit...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-98756-9 |
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| Summary: | Abstract 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. |
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| ISSN: | 2045-2322 |