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多尺度特征和Transformer全局学习融合的发动机剩余寿命预测

陈俊英 席月芸 李朝阳

陈俊英, 席月芸, 李朝阳. 多尺度特征和Transformer全局学习融合的发动机剩余寿命预测. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230634
引用本文: 陈俊英, 席月芸, 李朝阳. 多尺度特征和Transformer全局学习融合的发动机剩余寿命预测. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230634
Chen Jun-Ying, Xi Yue-Yun, Li Zhao-Yang. Prediction of aeroengine remaining life by combining multi-scale local features and transformer global learning. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230634
Citation: Chen Jun-Ying, Xi Yue-Yun, Li Zhao-Yang. Prediction of aeroengine remaining life by combining multi-scale local features and transformer global learning. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c230634

多尺度特征和Transformer全局学习融合的发动机剩余寿命预测

doi: 10.16383/j.aas.c230634
基金项目: 国家自然科学基金(62103316), 陕西省自然科学基金(2023-JC-YB-562)资助
详细信息
    作者简介:

    陈俊英:西安建筑科技大学信息与控制工程学院副教授. 2019年作为访问学者在澳大利亚新南威尔士大学进行学术交流. 于2010年在西安交通大学获得计算机科学与技术博士学位. 主要研究方向为机器学习、智能检测和视觉感知信息处理. 本文通信作者. E-mail: chenjy@xauat.edu.cn

    席月芸:西安建筑科技大学信息与控制工程学院硕士研究生. 主要研究方向为预测与健康管理系统. E-mail: yueyun@xauat.edu.cn

    李朝阳:西安建筑科技大学信息与控制工程学院硕士研究生.主要研究方向为工业缺陷目标检测. E-mail: nicholas@xauat.edu.cn

  • 中图分类号: Y

Prediction of Aeroengine Remaining Life by Combining Multi-scale Local Features and Transformer Global Learning

Funds: Supported by the National Natural Science Foundation of China (62103316) and Nature Science Foundation of Shaanxi Province (2023-JC-YB-562)
More Information
    Author Bio:

    CHEN Jun-Ying Associate Professor at the College of Information and Control Engineering, Xi'an University of Architecture and Technology. In 2019, she was a visiting scholar at the University of New South Wales in Australia for academic exchange. She obtained her Ph.D. degree in Computer Science and Technology from Xi'an Jiaotong University in 2010. Her research interest covers machine learning, intelligent detection, and visual perception information processing. Corresponding author of this paper

    XI Yue-Yun Master student at College of Information and Control Engineering, Xi'an University of Architecture and Technology. Her research interest covers prediction and health management systems

    LI Zhao-Yang Master student at College of Information and Control Engineering, Xi'an University of Architecture and Technology. His research interest covers industrial defect detection

  • 摘要: 飞机发动机剩余寿命的准确预测对确保其安全性和可靠性至关重要. 在基于多传感器检测数据预测时, 需解决局部特征提取问题以全面捕捉设备在不同时间尺度下的退化趋势, 并需解决时间序列中各元素之间长期依赖性的全局学习问题. 因此, 提出了结合多尺度局部特征增强单元(Multi-Sacle Local Feature Enhancement Unit, MSLFU_BLOCK)和Transformer编码器的预测模型, 称之为MS_Transformer. MSLFU_BLOCK利用堆叠的因果卷积逐层从时间序列数据中提取多尺度局部信息, 同时避免了传统卷积计算中固有的未来数据泄漏问题. 随后, Transformer编码器通过其自注意机制进一步捕获时间序列数据中的短期和长期依赖关系. 通过将多尺度局部特征增强单元与Transformer编码器相结合, 提出的MS_Transformer全面捕捉了时间序列数据中的局部和全局模式. 在广泛使用的Commercial Modular Aero-Propulsion System Simulation(C-MAPSS)基准数据集上进行的消融和预测实验验证了模型的合理性和有效性. 与13个先进预测模型的比较分析表明, MS_Transformer模型在操作条件更复杂的FD002和FD004数据集上的RMSE和Score指标优于其他模型, 同时在四个数据集上的平均性能最优. 该研究为发动机剩余寿命预测提供了更为可靠的解决方案.
  • 图  1  MS_Transformer寿命预测模型结构图

    Fig.  1  Architecture of the MS_Transformer life prediction model

    图  2  传统卷积与因果卷积示意图

    Fig.  2  Diagrams of traditional convolution and causal convolution

    图  3  多尺度局部特征增强单元示意图

    Fig.  3  Diagrams of multi-scale local feature enhancement unit

    图  4  状态参数变化曲线示意图

    Fig.  4  Illustration of state parameter variation curve

    图  5  测试集上预测值与真实值对比图

    Fig.  5  Comparison of predicted values and ground truth on the test set

    图  6  发动机剩余使用寿命全周期预测评估

    Fig.  6  Full lifecycle predictive evaluation of engine remaining useful life

    表  1  C-MAPSS数据集的属性

    Table  1  Attributes of the C-MAPSS dataset

    Parameters FD001 FD002 FD003 FD004
    Training sequences 100 260 100 249
    Testing sequences 100 259 100 248
    Operating conditions 1 6 1 6
    Fault modes 1 1 2 2
    Training size 20632 53760 24721 61250
    Test size(default) 13097 33992 16597 41215
    下载: 导出CSV

    表  2  与先进方法相比较

    Table  2  Comparison with State-of-the-Art methods

    MethodsFD001FD002FD003FD004Average
    RMSEScoreRMSEScoreRMSEScoreRMSEScoreRMSEScore
    LSTM (2017)[7]16.1433824.49171816.1885228.17223821.251286.5
    DCNN (2018)[11]12.6127422.36402012.6428423.31502717.732401.25
    HDNN (2019)[14]13.0224515.241282.4212.22287.7218.161527.4214.66835.64
    AGCNN (2021)[18]12.42225.5119.43149213.39227.0921.50339216.681334.15
    GCU_Transformer (2021)[32]11.2722.8111.4224.8617.59
    BiGRU-TSAM (2022)[20]12.56213.3518.942264.1312.45232.8620.473610.3416.101580.17
    IDMFFN (2022)[13]12.18204.6919.171819.4211.89205.5421.723338.8416.241392.12
    MTSTAN (2023)[24]10.97175.3616.811154.3610.90188.2218.851446.2914.38741.06
    Encoder-Attention (2023)[21]10.35183.7515.821008.0811.34219.6317.351751.2313.72790.67
    MSIDSN (2023)[23]11.74205.5518.262046.6512.04196.4222.482910.7316.131339.83
    ATCN (2024)[26]11.48194.2515.821210.5711.34249.1917.81934.8614.11897.22
    MHT (2024)[33]11.92215.213.7746.710.63150.517.73157213.50671.1
    MachNet (2024)[34]11.04176.8224.52332610.59161.2628.86591618.75252395.02
    Ours11.79224.3611.98608.8811.95225.0514.471072.3812.55532.67
    下载: 导出CSV

    表  3  消融实验结果

    Table  3  Results of ablation experiment

    MethodsFD001FD002FD003FD004
    RMSEScoreRMSEScoreRMSEScoreRMSEScore
    MS_Transformer11.79224.3611.98608.8811.95225.0514.471072.38
    MS (CNN) _Transformer12.82254.3613.721098.0913.80325.0515.931372.87
    MS_Transformer (w/o MS)13.20275.5915.781430.9014.45445.5118.481754.22
    MS_Transformer (w/o s & MS)13.91298.1815.911497.4116.10552.6119.031992.69
    下载: 导出CSV

    表  4  不同窗口长度(L) 对应的预测指标值

    windows lengthFD001FD002FD003FD004
    RMSEScoreRMSEScoreRMSEScoreRMSEScore
    L=3012.89264.7814.381011.0413.73279.9917.201858.13
    L=4012.67268.0713.42854.8812.21213.1316.741676.82
    L=5011.93212.9612.94724.1212.31255.2015.561375.81
    L=6011.79224.3611.98608.8811.95225.0514.471072.38
    L=7012.23242.8611.75587.6712.59266.96114.261093.49
    下载: 导出CSV

    表  5  不同因果卷积层数对应的预测指标值

    Table  5  Predictive metric values corresponding to different numbers of causal convolution layers

    NumberFD001FD002FD003FD004
    RMSEScoreRMSEScoreRMSEScoreRMSEScore
    112.28270.3312.64749.4212.60278.9916.311887.34
    211.79224.3611.98608.8811.95225.0514.471072.38
    313.02270.3314.981225.9814.19367.1617.302185.46
    下载: 导出CSV

    表  6  不同数量的encoder layer对应的预测指标值

    Table  6  Predictive metric values corresponding to different numbers of encoder layers

    NumberFD001FD002FD003FD004
    RMSEScoreRMSEScoreRMSEScoreRMSEScore
    111.79224.3611.98608.8811.95225.0514.471072.38
    211.35210.2512.78785.3211.56230.3216.721785.03
    311.95223.2512.58735.3211.86235.4615.721685.03
    下载: 导出CSV
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  • 收稿日期:  2023-10-12
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