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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月21日

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  • Junying Hu,
  • Xu Jiang,
  • Huan Xu,
  • Ke Zhang

摘要

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.