Data-model Interactive Remaining Useful Life Prediction Technologies for Stochastic Degrading Devices With Big Data
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摘要: 面向大数据背景下随机退化设备剩余寿命(Remaining useful life, RUL)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.Abstract: Focused on the realistic desire to the remaining useful life (RUL) prediction of stochastic degrading devices with big data, according to the characteristics of the big monitoring data of stochastic degrading devices and the core issue quantifying the uncertainty in the RUL prediction, this paper provides deep analysis of basic principles and advances of classical solution avenues to RUL prediction of stochastic degrading devices with big data from the data-driven viewpoint. The reviewed methods mainly include hybrid techniques based on physical model and data, machine learning method based techniques, statistical data-driven techniques, and the combination of machine learning methods and statistical data-driven methods. At the meanwhile, the limitations and common problems in existing studies are dissected. As for these limitations, taking the big monitoring data from multi-source sensors as an example, this paper presents a data-model interaction perspective to solve the RUL prediction problem for stochastic degrading devices with big data. The application to multi-source monitoring data of aero-engines preliminarily verifies the feasibility and effectiveness of this presented data-model interaction idea. Finally, inspired by the presented data-model interaction idea, it will be beneficial to tightly holding the main line of the RUL prediction uncertainty quantification by synthesizing advantages of intelligent methods and statistical data driven methods. As such, this paper discusses several key scientific issues for RUL prediction of stochastic degrading devices with big data, including the interactive collaboration idea between deep learning and stochastic degradation modeling, the mapping mechanism between the big monitoring data and the RUL with the prediction uncertainty quantification, RUL prediction issues under non-ideal data, etc.
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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 
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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
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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 Score 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.
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