[1]廖雪超,陈海力,钟实.基于DCNN-Informer的航空发动机寿命预测方法[J].计算机技术与发展,2025,(05):76-81.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0395]
　LIAO Xue-chao,CHEN Hai-li,ZHONG Shi.Approach for Aircraft Engine Life Prediction Based on DCNN-Informer[J].,2025,(05):76-81.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0395]
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基于DCNN-Informer的航空发动机寿命预测方法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年05期
页码:
76-81
栏目:
人工智能
出版日期:
2025-05-10

文章信息/Info

Title:
Approach for Aircraft Engine Life Prediction Based on DCNN-Informer
文章编号:
1673-629X(2025)05-0076-06
作者:
廖雪超1; 2; 陈海力1; 2; 钟实3
1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065;
2. 智能信息处理与实时工业系统湖北省重点实验室,湖北 武汉 430065;
3. 武汉钢铁股份有限公司 设备部技术室,湖北 武汉 430065
Author(s):
LIAO Xue-chao1; 2; CHEN Hai-li1; 2; ZHONG Shi3
1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems,Wuhan 430065,China;
3. Equipment Department Technical Office,Wuhan Iron and Steel Co. ,Ltd. ,Wuhan 430065,China
关键词:
深度学习; 剩余寿命预测; Informer; 卷积神经网络; 航空发动机
Keywords:
deep learning; remaining useful life prediction; Informer; convolutional neural networks; aircraft engines
分类号:
TP183
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0395
摘要:
故障预测与健康管理(PHM)在工业工程中发挥了重要作用,剩余使用寿命(RUL)预测对于维护策略的制定和减少工业损失至关重要。 针对航空发动机退化特征复杂性不断增加,导致发动机剩余寿命预测精度低的问题,该文利用卷积神经网络(CNN)提取时间序列数据的高维空间特征,并结合 Informer 的自注意力机制对这些特征进行全局建模,从而充分提取时间维度的信息。 此外,为进一步提高模型的准确性和泛化能力,设计了发动机二次训练框架,数据集按发动机进行分组,将每个分组的数据分别送入 CNN-Informer 模型进行二次训练,以得到针对每台发动机的个性化模型。 最后,采用DCNN-Informer 模型中的二阶指数平滑滤波算法对模型的预测结果进行滤波,以提高预测的稳定性和准确性。 实验结果表明,该预测模型在 RUL 预测方面具有明显优势,相较于现有模型,其预测性能更为优越。
Abstract:
Prognostics and Health Management ( PHM) plays a crucial role in industrial engineering,where predicting the remaining useful life (RUL) is essential for formulating maintenance strategies and reducing industrial losses,particularly for aircraft engines. Due to the increasing complexity of degradation characteristics in aircraft engines,leading to low accuracy in RUL prediction,we utilize conv-olutional neural networks ( CNN) to extract high - dimensional spatial features of time series data, and globally model them in combination with Informer’s self-attention mechanism,so as to fully extract time dimension information. Moreover,to further enhance the model’s accuracy and generalization ability, a secondary training framework for engines is designed. This framework groups the dataset by engine and feeds each group’s data into the CNN-Informer model for secondary training,resulting in personalized models for each engine. Finally,a second-order exponential smoothing filter algorithm is used to filter the model’s prediction results,improving pre-diction stability and accuracy. Experimental results demonstrate that the proposed prediction model has significant advantages in RUL prediction compared to existing models,with superior predictive performance.

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更新日期/Last Update: 2025-05-10