基于随机Wiener过程的航空发动机剩余寿命预测

doi:10.7527/S1000-6893.2014.0312

固体力学与飞行器总体设计

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基于随机Wiener过程的航空发动机剩余寿命预测

刘君强, 谢吉伟, 左洪福, 张马兰

南京航空航天大学民航学院, 南京 211106

收稿日期:2014-03-12 修回日期:2014-11-05 出版日期:2015-02-15 发布日期:2014-11-15
通讯作者: 刘君强,Tel.: 025-84896260 E-mail: liujunqiang@nuaa.edu.cn E-mail:liujunqiang@nuaa.edu.cn
作者简介:刘君强男,博士,讲师。主要研究方向:交通信息工程及控制、航空发动机健康管理等。Tel:025-84896260 E-mail:liujunqiang@nuaa.edu.cn;谢吉伟男,硕士研究生。主要研究方向:交通信息工程及控制。E-mail:xiejiwei@nuaa.edu.cn;左洪福男,博士,教授,博士生导师。主要研究方向:可靠性工程、维修理论、故障诊断与监控等。Tel:025-84893550 E-mail:rms@nuaa.edu.cn;张马兰女,硕士研究生。主要研究方向:交通信息工程及控制。E-mail:zhangmalan@126.com
基金资助:
国家自然科学基金(61232002,60939003);中国博士后科学基金(2012M1081,2013T60537);江苏省博士后科学基金(1031107C);中央高校基本科研业务专项资金(NS2014066);江苏高校哲学社会科学研究项目(2014SJD041)

Residual lifetime prediction for aeroengines based on Wiener process with random effects

LIU Junqiang, XIE Jiwei, ZUO Hongfu, ZHANG Malan

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2014-03-12 Revised:2014-11-05 Online:2015-02-15 Published:2014-11-15
Supported by:
National Natural Science Foundation of China (61232002, 60939003); China Postdoctoral Science Foundation (2012M1081, 2013T60537); Postdoctoral Science Foundation of Jiangsu Province of China(1031107C); Fundamental Research Funds for the Central Universities (NS2014066); Philosophy and Social Science Research Projects in Colleges and Universities in Jiangsu (2014SJD041)

摘要/Abstract

摘要：

针对目前剩余寿命(RL)预测方法没有综合考虑发动机个体性能退化的差异性和多阶段性的问题,提出了基于多阶段性能退化模型预测航空发动机剩余寿命的方法。首先,该方法采用多阶段Wiener过程对航空发动机进行退化建模,并假设退化模型参数服从随机分布来描述发动机个体的差异性。然后,根据历史性能退化数据与历史失效时间数据,利用期望最大化算法对模型参数的先验分布进行估计。当获得单台发动机的实时退化数据后,使用Bayesian方法对模型参数进行更新,从而实时更新航空发动机的RL分布,最终实现对单台航空发动机的RL预测。实验结果表明,该方法预测精度较高,能为航空发动机维修计划的制定提供依据。

关键词: Wiener过程, 剩余寿命, 信息融合, 期望最大化算法, Bayesian方法

Abstract:

There are few models in consideration of the unit-to-unit variability and multi-phase variability simultaneously in the residual lifetime (RL) prediction for aeroengines, so propose a Wiener process-based degradation modeling method for RUL prediction considering the above-mentioned factors. First, this method models the degradation path for aeroengines conditioned on multi-phased Wiener process. Then, historical degradation data and failure-time data are fused to derive the prior distribution of the unknown parameters for degradation model and expectation maximization algorithm is used to estimate the hyper-parameters of the prior distribution. Once the real-time degradation data are available, posterior distribution of the parameters is updated through a Bayesian method. Lastly, the RL prediction is obtained based on the updated parameters. Experiment shows that the method can improve the accuracy of the RL prediction and can provide the decision-maker with enough information to perform necessary maintenance actions prior to the failure.

Key words: Wiener process, residual lifetime, information fused, expectation maximization algorithm, Bayesian method

中图分类号:

V267⁺.4

刘君强, 谢吉伟, 左洪福, 张马兰. 基于随机Wiener过程的航空发动机剩余寿命预测[J]. 航空学报, 2015, 36(2): 564-574.

LIU Junqiang, XIE Jiwei, ZUO Hongfu, ZHANG Malan. Residual lifetime prediction for aeroengines based on Wiener process with random effects[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(2): 564-574.

参考文献

[1] Zeng S K, Pecht M G, Wu J. Staus and perspectives of prognostics and health management technologies[J]. Acta Aeronautica et Astronautica Sinica, 2005, 26(5): 626-632 (in Chinese). 曾声奎, Pecht M G, 吴际. 故障预测与健康管理(PHM)技术的现状与发展[J]. 航空学报, 2005, 26(5): 626-632.

[2] Pecht M G. Prognostics and health management of electronics[M]. Hoboken, New Jersey: Wiley Online Library, 2008: 1-5.

[3] Jardine A K S, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance[J]. Mechanical Systems and Signal Processing, 2006, 20(7): 1483-1510.

[4] Virkler D A, Hillberry B M, Goel P K. The statistical nature of fatigue crack propagation[J]. Journal of Engi-neering Materials and Technology, 1979, 101(2): 148-153.

[5] Zhang Y J, Wang Z Z. Cumulative damage model and parameter estimate about a kind of time-sharing redundant system[J]. Acta Physica Sinica, 2009, 58(9): 6074-6079 (in Chinese). 张永进, 汪忠志. 一类分时冗余系统的累伤可靠性模型及其参数估计[J]. 物理学报, 2009, 58(9): 6074-6079.

[6] Park C, Padgett W J. Accelerated degradation models for failure based on geometric Brownian motion and Gamma processes[J]. Lifetime Data Analysis, 2005, 11(4): 511-527.

[7] Peng W, Li Y F, Yang Y J, et al. Inverse Gaussian pro-cess models for degradation analysis: a Bayesian perspective[J]. Reliability Engineering and System Safety, 2014, 130(10): 175-189.

[8] Si X S, Wang W, Hu C H, et al. Remaining useful life estimation—a review on the statistical data driven ap-proaches[J]. European Journal of Operational Research, 2011, 213(1): 1-14.

[9] Nicolai R P, Dekker R, van Northwick J M. A compari-son of models for measurable deterioration: an application to coatings on steel structures[J]. Reliability Engineering and System Safety, 2007, 92(12): 1635-1650.

[10] Peng B H, Zhou J L, Pan Z Q. Bayesian method for reliability assessment of products with Wiener process degradation[J]. Systems Engineering-Theory and Practice, 2010, 30(3): 543-549 (in Chinese). 彭宝华, 周经伦, 潘正强. Wiener 过程性能退化产品可靠性评估的 Bayesian方法[J]. 系统工程理论与实践, 2010, 30(3): 543-549.

[11] Peng B H, Zhou J L, Sun Q, et al. Residual lifetime prediction of products based on fusion of degradation data and lifetime data[J]. System Engineering and Electronics, 2011, 33(5): 1073-1078 (in Chinese). 彭宝华, 周经伦, 孙权, 等. 基于退化与寿命数据融合的产品剩余寿命预测[J]. 系统工程与电子技术, 2011, 33(5): 1073-1078.

[12] Wang X L, Guo B, Cheng Z J. Reliability assessment of products with Wiener process degradation by fusing multiple information[J]. Acta Electronica Sinica, 2012, 40(5): 977-982 (in Chinese). 王小林, 郭波, 程志君. 融合多源信息的维纳过程性能退化产品的可靠性评估[J]. 电子学报, 2012, 40(5): 977-982.

[13] Wang X. Wiener processes with random effects for degradation data[J]. Journal of Multivariate Analysis, 2010, 101(2): 340-351.

[14] Gebraeel N Z, Lawley M A, Li R, et al. Residual life distributions from component degradation signals: a Bayesian approach[J]. IEEE Transactions on Reliability, 2005, 37(6): 543-557.

[15] Wang X L, Guo B, Cheng Z Z. Real-time reliability evaluation of equipment based on separated-phase Wiener-Einstein process[J]. Journal of Central South University: Science and Technology, 2012, 43(2): 534-540 (in Chinese). 王小林, 郭波, 程志君. 基于分阶段Wiener-Einstein过程设备的实时可靠性评估[J]. 中南大学学报: 自然科学版, 2012, 43(2): 534-540.

[16] Chikkara R S, Folks J K. The inverse Gaussian distribution[M]. New York: Marcell Dekker, 1989: 7-10.

[17] Yan W A, Song B W, Mao Z Y. Empirical Bayesian estimation of Wiener process with integrated degradation data and life data[C]//IEEE 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). New York: IEEE, 2013: 183-188.

[18] Xu A C, Tang Y C. EM algorithm for degradaton data analysis[J]. Journal of East China Normal University, 2010, 5(5): 38-48.

[19] Mao S S, Tang Y C. The Bayesian statistics[M]. Beijing: China Statistics Press, 2012: 10-16 (in Chinese). 茆诗松, 汤银才. 贝叶斯统计[M]. 北京: 中国统计出版社, 2012: 10-16.

[20] Zhou M J. Research on aero-engine performance margin design considering performance deterioration in utility[J]. Journal of Aerospace Power, 2008, 23(10): 1868-1874 (in Chinese). 周茂军. 考虑性能衰退的航空发动机总体性能裕度设计研究[J]. 航空动力学报, 2008, 23(10): 1868-1874.

[21] Ren S H, Zuo H F, Bai F. Real-time performance reliability prediction for civil aviation engines based on Brownian motion with drift[J]. Journal of Aerospace Power, 2009, 24(12): 2796-2801 (in Chinese). 任淑红, 左洪福, 白芳. 基于带漂移的布朗运动的民用航空发动机实时性能可靠性预测[J]. 航空动力学报, 2009, 24(12): 2796-2801.

E-mail：hkxb@buaa.edu.cn

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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

基于随机Wiener过程的航空发动机剩余寿命预测

Residual lifetime prediction for aeroengines based on Wiener process with random effects

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