融合多传感器数据的发动机剩余寿命预测方法

doi:10.7527/S1000-6893.2019.23312

固体力学与飞行器总体设计

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融合多传感器数据的发动机剩余寿命预测方法

任子强, 司小胜, 胡昌华, 王玺

火箭军工程大学导弹工程学院, 西安 710025

收稿日期:2019-07-24 修回日期:2019-08-02 出版日期:2019-12-15 发布日期:2019-09-02
通讯作者: 司小胜 E-mail:sxs09@mail.tsinghua.edu.cn
基金资助:
国家自然科学基金（61922089，61773386，61833016，61573365）

Remaining useful life prediction method for engine combining multi-sensors data

REN Ziqiang, SI Xiaosheng, HU Changhua, WANG Xi

School of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China

Received:2019-07-24 Revised:2019-08-02 Online:2019-12-15 Published:2019-09-02
Supported by:
National Natural Science Foundation of China (61922089, 61773386, 61833016, 61573365)

摘要/Abstract

摘要： 针对基于单一传感器数据的剩余寿命预测方法存在数据利用率低和预测精度不高的问题，论文提出了一种融合多传感器数据的发动机剩余寿命预测方法。首先将多个传感器数据融合成一个复合健康指标来表征发动机的退化性能，采用线性维纳过程对复合健康指标进行退化建模，通过极大似然估计方法确定模型参数，进而得到发动机的预测寿命。为了确定融合系数，提出了一种利用真实寿命与预测寿命的预测均方误差最小化的方法。融合系数确定后，基于训练发动机历史寿命数据，确定出模型参数的离线估计值；然后利用Bayesian公式，同时结合发动机的实时监测数据与参数的先验分布对模型参数进行实时更新，接着在首达时间的意义下推导出剩余寿命的概率分布，进而实现了发动机的剩余寿命在线预测。最后，选择商用模块化航空推进系统仿真数据集进行数值仿真实验，结果表明：相较于基于单一传感器的方法，论文所提方法能够提高剩余寿命预测的准确性，其剩余寿命预测的相对均方误差降低了2%左右。

关键词: 复合健康指标, 线性维纳过程, 极大似然估计, 贝叶斯参数更新, 剩余寿命预测

Abstract: For remaining useful life prediction method based on single sensor data, there is a problem of low data utilization and low prediction accuracy. This paper proposes a method for predicting the remaining useful life of an engine that combines multi-sensors data. Firstly, multiple sensors data are fused into a composite health index to characterize the degraded performance of the engine. The linear Wiener process is used to model the composite health index. The model parameters are determined by the maximum likelihood estimation method, and then the predicted life of the engine is obtained. In order to determine the fusion coefficient, a method for minimizing the predicted mean square error between the actual life and the predicted life is proposed. After the fusion coefficient is determined, based on the historical life data of the training engine, the offline estimation value of the model parameters is determined. Then, using the Bayesian formula, combined with the real-time monitoring data of the engine and the prior distribution of the parameters, the model parameters are updated in real time, and the probability distribution of the remaining useful life is derived based on the first hitting time, thereby realizing the online prediction of the remaining useful life of the engine. Finally, the commercial modular aero-propulsion system simulation data set is selected for numerical simulation experiments. The results show that compared with the single sensor-based method, the proposed method can improve the accuracy of the remaining useful life prediction, and the relative mean square error of remaining useful life prediction is reduced by about 2%.

Key words: composite health index, linear Wiener process, maximum likelihood estimation, Bayesian parameter update, remaining useful life prediction

中图分类号:

V239

任子强, 司小胜, 胡昌华, 王玺. 融合多传感器数据的发动机剩余寿命预测方法[J]. 航空学报, 2019, 40(12): 223312-223312.

REN Ziqiang, SI Xiaosheng, HU Changhua, WANG Xi. Remaining useful life prediction method for engine combining multi-sensors data[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(12): 223312-223312.

参考文献 23

[1]	HUANG Z, XU Z, WANG W, et al. Remaining useful life prediction for a nonlinear heterogeneous wiener process model with an adaptive drift[J]. IEEE Transactions on Reliability, 2015, 64(2):687-700.
[2]	周东华, 魏慕恒, 司小胜. 工业过程异常检测、寿命预测与维修决策的研究进展[J]. 自动化学报, 2013, 39(6):711-722. ZHOU D H, WEI M H, SI X S. A survey on anomaly detection, life prediction and maintenance decision for industrial processes[J]. Acta Automatica Sinica, 2013, 39(6):711-722(in Chinese).
[3]	孔祥芬, 蔡峻青,张利寒,等. 大数据在航空系统的研究现状与发展趋势[J]. 航空学报, 2018, 39(12):8-23. KONG X F, CAI J Q, ZHANG L H, et al. Research status and development trend of big data in aviation system[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(12):8-23(in Chinese).
[4]	银越千, 金海良,陈璇. 涡轴/涡桨发动机压气机流动特点与发展趋势[J]. 航空学报, 2017, 38(9):35-50. YIN Y Q, JIN H L, CHEN X. Flow feature and developing trends of compressor in turboshaft/turboprop engine[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(9):35-50(in Chinese).
[5]	赵广社, 吴思思, 荣海军. 多源统计数据驱动的航空发动机剩余寿命预测方法[J]. 西安交通大学学报, 2017, 51(11):150-155. ZHAO G S, WU S S, RONG H J. A multi-source statistics data-driven method for remaining useful life prediction of aircraft engine[J]. Journal of Xi'an Jiaotong University, 2017, 51(11):150-155(in Chinese).
[6]	唐王. 数据驱动的航空发动机余寿预测方法[D]. 南京:南京航空航天大学, 2018:1-20. TANG W. Data-driven aero-engine remaining useful life prediction method[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2018:1-20(in Chinese).
[7]	CHEHADE A, SONG C, LIU K, et al. A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes[J]. Journal of Quality Technology, 2018,50(2):150-165.
[8]	周经伦, 厉海涛, 刘学敏, 等. 维纳过程寿命预测的一种自助法[J]. 系统工程理论与实践, 2011,31(8):1588-1592. ZHOU J L, LI H T, LIU X M, et al. A bootstrap method of life prediction based on Wiener process[J]. Systems Engineering Theory & Practice, 2011, 31(8):1588-1592(in Chinese).
[9]	施权, 胡昌华, 司小胜, 等. 考虑执行器性能退化的控制系统剩余寿命预测方法[J]. 自动化学报, 2019, 45(5):941-952. SHI Q, HU C H, SI X S, et al. Remaining useful lifetime prediction method of controlled systems considering performance degradation of actuator[J]. Acta Automatica Sinica, 2019, 45(5):941-952(in Chinese).
[10]	CAESARENDRA W, WIDODO A, THOM P H, et al. Combined probability approach and indirect data-driven method for bearing degradation prognostics[J]. IEEE Transactions on Reliability, 2011,60(1):14-20.
[11]	GEBRAEEL N Z, LAWLEY M A, LI R, et al. Residual-life distributions from component degradation signals:A Bayesian approach[J]. IIE Transactions, 2005, 37(6):543-557.
[12]	SAXENA A, GOEBEL K, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]//International Conference on Prognostics and Health Management. Piscataway, NJ:IEEE Press, 2008:1-9.
[13]	LIU K, GEBRAEEL N Z, SHI J. A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis[J]. IEEE Transactions on Automation Science and Engineering, 2013,10(3):652-664.
[14]	YAN H, LIU K, ZHANG X, et al. Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions[J]. IEEE Transactions on Reliability, 2016,65(3):1416-1426.
[15]	SONG C, LIU K, ZHANG X. Integration of data-level fusion model and kernel methods for degradation modeling and prognostic analysis[J]. IEEE Transactions on Reliability, 2018, 67(2):640-650.
[16]	LIU K, HUANG S. Integration of data fusion methodology and degradation modeling process to improve prognostics[J]. IEEE Transactions on Automation Science and Engineering, 2016,13(1):344-354.
[17]	LIU K, CHEHADE A, SONG C. Optimize the signal quality of the composite health index via data fusion for degradation modeling and prognostic analysis[J]. IEEE Transactions on Automation Science and Engineering, 2017,14(3):1504-1514.
[18]	FANG X, PAYNABAR K, GEBRAEEL N. Multistream sensor fusion-based prognostics model for systems with single failure modes[J]. Reliability Engineering & System Safety, 2017,159:322-331.
[19]	SI X, WANG W, CHEN M, et al. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution[J]. European Journal of Operational Research, 2013, 226(1):53-66.
[20]	SI X, WANG W, HU C, et al. Remaining useful life estimation based on a nonlinear diffusion degradation process[J]. IEEE Transactions on Reliability, 2012, 61(1):50-67.
[21]	司小胜, 胡昌华. 数据驱动的设备剩余寿命预测理论及应用[M]. 北京:国防工业出版社, 2016:122-123. SI X S, HU C H. Data-driven remaining useful life prediction theory and applications for equipment[M]. Beijing:National Defense Industry Press, 2016:122-123(in Chinese).
[22]	SAXENA A,GOEBEL. C-MAPSS data set(2019-07-20)[2019-07-23]. https://www.dssz.com/2148128.html.
[23]	ZHANG Y, XIONG R, HE H, et al. Lithium-ion battery remaining useful life prediction with Box-Cox transformation and Monte Carlo simulation[J]. IEEE Transactions on Reliability Industrial Electronics, 2018, 1(1):1-8.

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

融合多传感器数据的发动机剩余寿命预测方法

Remaining useful life prediction method for engine combining multi-sensors data

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