| 张加劲.基于注意力机制和 CNN-BiLSTM 模型的
航空发动机剩余寿命预测[J].电子测量与仪器学报,2022,36(8):231-237 |
| 基于注意力机制和 CNN-BiLSTM 模型的
航空发动机剩余寿命预测 |
| Remaining useful life estimation of aeroengine based onCNN-BiLSTM and attention mechanism |
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| DOI： |
| 中文关键词: 航空发动机 剩余寿命 卷积神经网络 注意力机制 双向长短期记忆网络 |
| 英文关键词:aeroengine remaining useful life convolution neural network attention mechanism bidirectional long short-term
memory network |
| 基金项目: |
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| 摘要点击次数: 895 |
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| 中文摘要: |
| 航空发动机作为飞机的主要动力源,其可靠性是保证飞机安全的关键。 剩余使用寿命预测对于提高航空发动机的可用
性和降低其寿命周期成本具有重要意义。 针对现有的预测算法存在对航空发动机多维数据特征提取不足的问题,提出了一种
基于注意力机制的卷积神经网络和双向长短期网络融合模型。 首先,采用卷积神经网络提取特征和双向长短期记忆网络获取
特征中的长短期依赖关系;其次,使用注意力机制来突出特征中的重要部分,提高模型预测的准确率。 为验证所提出方法的有
效性,在 C-MAPSS 数据集上进行了实验。 实验表明,模型可以准确地预测出航空发动机的剩余使用寿命,并比传统方法有着更
高的预测精度。 |
| 英文摘要: |
| As the main power source for aircrafts, the reliability of an aeroengine is critical for ensuring the safety of aircrafts. remaining
useful life (RUL) prediction is of great importance for improving the availability of an aero engine and reducing its life cycle cost. For
the problem of the shortcomings of existing estimation algorithms in the extraction of multi-dimensional data features, this paper proposes
an attention-based CNN-BiLSTM model for RUL estimation. This model using CNN layers to extra feature and BILSTM network can
capture the short-term and long-term dependencies of the extracted feature. Afterwards, attention mechanism layer is used to highlight
the important features in order to improve model performance. To evaluate the effectiveness of our approach, experiments are carried out
on CMAPSS datasets and its result shows that the performance of the proposed approach is superior to other traditional approaches. |
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