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大数据下数模联动的随机退化设备剩余寿命预测技术

李天梅 司小胜 刘翔 裴洪

李天梅, 司小胜, 刘翔, 裴洪. 大数据下数模联动的随机退化设备剩余寿命预测技术. 自动化学报, 2021, 45(x): 1−23 doi: 10.16383/j.aas.c201068
引用本文: 李天梅, 司小胜, 刘翔, 裴洪. 大数据下数模联动的随机退化设备剩余寿命预测技术. 自动化学报, 2021, 45(x): 1−23 doi: 10.16383/j.aas.c201068
Li Tian-Mei, Si Xiao-Sheng, Liu Xiang, Pei Hong. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data. Acta Automatica Sinica, 2021, 45(x): 1−23 doi: 10.16383/j.aas.c201068
Citation: Li Tian-Mei, Si Xiao-Sheng, Liu Xiang, Pei Hong. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data. Acta Automatica Sinica, 2021, 45(x): 1−23 doi: 10.16383/j.aas.c201068

大数据下数模联动的随机退化设备剩余寿命预测技术

doi: 10.16383/j.aas.c201068
基金项目: 国家自然科学基金(62073336, 61922089, 61773386)资助
详细信息
    作者简介:

    李天梅:火箭军工程大学副教授, 主要研究方向为预测与健康管理, 剩余寿命智能预测. E-mail: tmlixjtu@163.com

    司小胜:火箭军工程大学教授, 主要研究方向为随机退化系统剩余寿命预测与健康管理, 随机退化建模, 预测维护. 本文通讯作者. E-mail: sxs09@mails.tsinghua.edu.cn

    刘翔:火箭军工程大学讲师, 主要研究方向为预测与健康管理, 剩余寿命智能预测. E-mail: liux_92@163.com

    裴洪:火箭军工程大学讲师, 主要研究方向为预测与健康管理, 剩余寿命智能预测. E-mail: ph2010hph@sina.com

Data-Model Interactive Remaining Useful Life Prediction Technologies for Stochastic Degrading Devices with Big Data

Funds: National Nature Science Foundation of P. R. China (62073336, 61922089, 61773386)
More Information
    Author Bio:

    LI Tian-Mei Associate professor in College of Missile Engineering, Rocket Force University of Engineering. Her research interest covers prognostics and health management, remaining useful life intelligent prediction

    SI Xiao-Sheng Professor at the Rocket Force University of Engineering. His research interest covers remaining useful life prediction and health management, stochastic degradation modeling and predictive maintenance. The corresponding author of the paper

    LIU Xiang Lecturer in College of Missile Engineering, Rocket Force University of Engineering. His research interest covers prognostics and health management, remaining useful life intelligent prediction

    PEI Hong Lecturer in College of Missile Engineering, Rocket Force University of Engineering. His research interest covers prognostics and health management, remaining useful life intelligent prediction

  • 摘要: 本文面向大数据背景下随机退化设备剩余寿命预测的现实需求, 结合随机退化设备监测大数据特点及剩余寿命预测不确定性量化这一核心问题, 深入分析了机理模型与数据混合驱动的剩余寿命预测技术、基于机器学习的剩余寿命预测技术、统计数据驱动的剩余寿命预测技术以及机器学习和统计数据驱动相结合的剩余寿命预测技术的基本研究思想和发展动态, 剖析了当前研究存在的局限性和共性难题. 针对存在的局限性和共性难题, 以多源传感监测大数据下剩余寿命预测问题为例, 提出了一种数模联动的大数据下随机退化设备剩余寿命预测解决思路, 并通过航空发动机多源监测数据初步验证了该思路的可行性和有效性. 最后, 借鉴数模联动思路, 综合考虑机器学习方法和统计数据驱动方法的优势, 紧紧扭住大数据背景下随机退化设备剩余寿命预测不确定性量化问题, 提出了大数据背景下深度学习与随机退化建模交互联动、监测大数据与剩余寿命及其预测不确定性映射机制、非理想大数据下的剩余寿命预测等亟待解决的关键科学问题.
  • 图  1  剩余寿命预测方法体系

    Fig.  1  The methodology of remaining useful life prediction

    图  2  完整的、碎片化的、稀疏的监测大数据示例

    Fig.  2  Examples of complete, fragment and sparse big data

    图  3  多源传感器剩余寿命预测数模联动解决方案与流程图

    Fig.  3  Idea and Flowchart of data-model interactive remaining useful life prediction with multi-source sensors

    表  1  皮尔逊相关系数对比结果

    Table  1  Comparative results of Pearson correlation coefficients

    健康指标 皮尔逊相关系数
    单一传感器 低压压气机出口总温度 (T24) 0.6753
    高压压气机出口总温度 (T30) 0.6440
    低压涡轮出口总温度 (T50) 0.7816
    高压压气机出口总压力 (P30) −0.7615
    高压压气机出口静压 (Ps30) 0.8106
    燃料流量与Ps30的比率 (phi) −0.7897
    旁路比率 (BRP) 0.7248
    出血焓 (htBleed) 0.6731
    高压涡轮冷却剂排放 (W31) −0.7141
    低压涡轮冷却剂排放 (W32) −0.7167
    本文数模联动复合健康指标 0.9002
    下载: 导出CSV

    表  2  失效时刻健康指标值的方差比较

    Table  2  Variance of health indices at failure time

    健康指标 方差值
    单一传感器 低压压气机出口总温度 (T24) 0.0274
    高压压气机出口总温度 (T30) 0.0176
    低压涡轮出口总温度 (T50) 0.0140
    高压压气机出口总压力 (P30) 0.0264
    高压压气机出口静压 (Ps30) 0.0154
    燃料流量与Ps30的比率 (phi) 0.0206
    旁路比率 (BRP) 0.0225
    出血焓 (htBleed) 0.0435
    高压涡轮冷却剂排放 (W31) 0.0220
    低压涡轮冷却剂排放 (W32) 0.0317
    复合健康指标[133] 0.0035
    复合健康指标[137] 0.0101
    本文数模联动复合健康指标 0.0013
    下载: 导出CSV

    表  3  剩余寿命预测性能比较

    Table  3  Comparative results in the performance of the remaining useful life prediction

    预测方法 Socre Accuracy MSE
    支持向量回归方法[65] 449 70 %
    基于案例的学习方法[71] 1389.26 44 %
    基于案例的推理方法[72] 216 67 % 176
    多目标深度置信网络集成方法[86] 334.23 226.20
    卷积神经网络[92] 1287 340
    循环神经网络[96] 219 59 % 155
    受限玻尔兹曼机+LSTM网络[104] 231 157.75
    基于长短时网络的编码-解码器[106] 256 67 % 164
    循环神经网络+自编码器[107] 245 70 %
    基于多损失编码器与卷积复合特征的两阶段深度学习方法[108] 208 133.86
    深度置信网络+后向传播神经网络+改进粒子滤波算法[140] 543 51 % 283
    深度置信网络+改进粒子滤波算法[140] 314 63 % 172
    线性Wiener随机过程方法 低压压气机出口总温度(T24) $1.32 \times 10 ^9$ 45 % 1193.76
    高压压气机出口总温度(T30) $2.96 \times 10 ^7$ 32 % 1288.29
    低压涡轮出口总温度(T50) 377.67 62 % 210.86
    高压压气机出口总压力(P30) 5109.67 53 % 420.48
    高压压气机出口静压(Ps30) 1328.63 61 % 296.03
    燃料流量与Ps30的比率(phi) 1442.09 57 % 325.20
    旁路比率(BRP) $2.59 \times 10 ^4$ 48 % 501.06
    出血焓(htBleed) 2847.74 30 % 669.43
    高压涡轮冷却剂排放(W31) $4.92 \times 10 ^4$ 48 % 458.40
    低压涡轮冷却剂排放(W32) 1564.21 46 % 427.19
    本文数模联动预测方法 95.87 81 % 68.29
    注: 表中“—”表示原文中没有计算并给出该指标值.
    下载: 导出CSV
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  • 收稿日期:  2020-12-25
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