Hu Junying, Jiang Xu, Xu Huan, Zhang Ke
School of Economics and Management, Hefei University, 230601, Hefei, People's Republic of China.
ECS Sales Department, Alibaba (Beijing) Software Services Co., Ltd, 100000, Beijing, People's Republic of China.
Sci Rep. 2025 Apr 21;15(1):13726. doi: 10.1038/s41598-025-98756-9.
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.
为了解决航空发动机故障预测中的类别不平衡问题，我们提出了一种集成自适应混合采样和双向长短期记忆网络（BiLSTM）的新颖框架。首先，提出了一种基于k均值的自适应采样策略，通过对少数类边界进行过采样和对冗余多数类簇进行欠采样来动态平衡数据集。其次，构建了一个利用BiLSTM的故障预测模型进行故障预测，该模型能够有效捕捉双向时间依赖性。对实际传感器数据进行的实验表明，该方法有效地提高了不平衡数据集中故障样本的识别率。
