基于Wiener过程的发动机多阶段剩余寿命预测

doi:10.13700/j.bh.1001-5965.2017.0383

北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (5): 1081-1087.doi: 10.13700/j.bh.1001-5965.2017.0383

基于Wiener过程的发动机多阶段剩余寿命预测

黄亮, 刘君强, 贡英杰

南京航空航天大学民航学院, 南京 211106

收稿日期:2017-06-06 出版日期:2018-05-20 发布日期:2018-05-29
通讯作者: 刘君强 E-mail:liujunqiang@nuaa.edu.cn
作者简介:黄亮,男,硕士研究生。主要研究方向:发动机健康管理;刘君强,男,博士,副教授。主要研究方向:民航信息管理、健康管理。
基金资助:
民航联合研究基金（U1533128）

Multi-phase residual life prediction of engines based on Wiener process

HUANG Liang, LIU Junqiang, GONG Yingjie

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

Received:2017-06-06 Online:2018-05-20 Published:2018-05-29

摘要/Abstract

摘要： 针对现阶段发动机的寿命预测研究没有考虑到非线性与多阶段的问题，提出了基于多阶段非线性Wiener过程的航空发动机实时剩余寿命预测的方法。该方法融合了同类型发动机的历史性能退化监测数据与个体发动机的实时监测数据。首先，考虑了发动机性能退化非线性的特点，并采用多阶段Wiener过程建立发动机的性能退化模型。然后，根据发动机的历史性能监测数据，利用极大似然估计和一维搜索方法进行参数先验分布的估计。再次，在先验分布和个体发动机的退化数据的基础上，用贝叶斯方法对参数分布更新。最后，得到个体发动机剩余寿命的实时预测值。通过实例验证本文方法预测的准确性。

关键词: 剩余寿命, 多阶段, 非线性, Wiener过程, 实时预测, 贝叶斯方法

Abstract: Due to the fact that the research on the life prediction of the engine does not take into account both the nonlinearity and the multi-stage problems at the present stage, a method for forecasting the residual life of aeroengines in real time based on multi-phase nonlinear Wiener process is proposed. This method can effectively fuse the historical performance degradation monitoring data of the same type of aeroengines with the real-time monitoring data of the individual aeroengine. Firstly, the nonlinearity of performance degradation is considered, and the multi-stage Wiener process was used to establish the performance degradation model of engine. Secondly, according to the historical performance monitoring data of the aeroengines, the prior distribution of parameters is estimated by using maximum likelihood estimation and one-dimensional search method. Thirdly, according to the real-time performance degradation data of individual aeroengine and the prior distribution, the Bayesian method is used to update the model parameters. Finally, the real-time predicted values of the residual life of the individual aeroengine are obtained. By the test of the actual data, the results show that the proposed method is accurate.

Key words: residual useful life, multi-phase, nonlinear, Wiener process, real-time prediction, Bayesian method

中图分类号:

V263

黄亮, 刘君强, 贡英杰. 基于Wiener过程的发动机多阶段剩余寿命预测[J]. 北京航空航天大学学报, 2018, 44(5): 1081-1087.

HUANG Liang, LIU Junqiang, GONG Yingjie. Multi-phase residual life prediction of engines based on Wiener process[J]. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2018, 44(5): 1081-1087.

参考文献

[1] PECHT M G.Prognostics and health management of electronics[M].Hoboken:Wiley Online Library,2008:1-19.
[2] GUAN Q,TANG Y,XU A.Objective Bayesian analysis accelerated degradation test based on Wiener process models[J].Applied Mathematical Modelling,2016,40(4):2743-2755.
[3] 谢吉伟,刘君强,王小磊.应用交互式多模型算法的设备剩余寿命预测[J].空军工程大学学报(自然科学版),2016,17(2):98-102.XIE J W,LIU J Q,WANG X L.A residual usefual lifetime prediction based on interacting multiple model algorithm[J].Journal of Air Force Engineering University(Natural Science Edition),2016,17(2):98-102(in Chinese).
[4] SI X S,WANG W,HU C H,et al.Remaining useful life estimation-A review of the statistical data driven approaches[J].European Journal of Operational Research,2011,213(1):1-14.
[5] FENG L,WANG H L,SI X S,et al.A state-space-based prognostic model for hidden and age-dependent nonlinear diffusion degradation process[J].IEEE Transactions on Automation Science and Engineering,2013,10(4):1072-1086.
[6] 彭宝华,周经伦,孙权.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.PENG B H,ZHOU J L,SUN Q.Residual lifetime prediction of products based on fusion of degradation data and lifetime data[J].Systems Engineering and Electronic,2011,33(5):1073-1078(in Chinese).
[7] GEBRAEEL N Z,ELWANY A H,JING P.Residual life predictions in the absence of prior degradation knowledge[J].IEEE Transactions on Reliability,2009,58(1):106-117.
[8] SI X S,WANG W,HU C H,et al.Remaining useful life estimation based on nonlinear diffusion degradation process[J].IEEE Transactions on Reliability,2012,61(1):50-67.
[9] PENG W,LI Y F,YANGY J,et al.Inverse Gaussian process models for degradation analysis:A Bayesian perspective[J].Reliability Engineering & System Safety,2014,130(1):175-189.
[10] WANG W.A two-stage prognosis model in condition based maintenance[J].European Journal of Operational Research,2007,182(3):1177-1187.
[11] 王小林,程志君,郭波.基于维纳过程金属化模电容器的剩余寿命预测[J].国防科技大学学报,2011,33(4):146-151.WANG X L,CHENG Z J,GUO B.Residual life forecasting of metallized film capacitor based on Wiener process[J].Journal of National University of Defense Technology,2011,33(4):146-151(in Chinese).
[12] 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.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).
[13] WANG W,CARR M,XU W,et al.A model for residual life prediction based on Brownian motion with an adaptive drift[J].Microelectronic Reliability,2011,51(2):285-293.
[14] SI X S,WANG W,HU C H,et al.Estimating remaining useful life with three-source variability in degradation modelling[J].IEEE Transactions on Reliability,2014,63(1):167-190.
[15] WANG X.Wiener processes with random effects for degradation data[J].Journal of Multivariate Analysis,2010,101(2):340-351.
[16] PARK C,PADGETT W J.Accelerated degradation models for failure based on geometric Brownian motion and Gamma processes[J].Life Time Data Analysis,2005,11(4):511-527.
[17] GEBRAEEL N Z.Sensory-updated residual life distributions for components with exponential degradation patterns[J].IEEE Transactions on Automation Science and Engineering,2006,3(4):382-392.
[18] 司小胜,胡昌华,周东华.带测量误差的非线性退化过程建模与剩余寿估计[J].自动化学报,2013,39(5):530-541.SI X S,HU C H,ZHOU D H.Nonlinear degradation process modeling and remaining useful life estimation subject to measurement error[J].Acta Automatica Sinica,2013,39(5):530-541(in Chinese).
[19] 刘君强,谢吉伟,左洪福,等.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 J Q,XIE J W,ZUO H F,et al.Residual lifetime prediction for aeroengines based on Wiener process with random effects[J].Acta Aeronautica et Astronautica Sinica,2015,36(2):564-574(in Chinese).
[20] 茆诗松,汤银才.贝叶斯统计[M].北京:统计出版社,2012:10-16.MAO S S,TANG Y C.The Bayesian statistics[M].Beijing:China Statistics Press,2012:10-16(in Chinese).

基于Wiener过程的发动机多阶段剩余寿命预测

Multi-phase residual life prediction of engines based on Wiener process

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	黄行蓉, 刘久周, 李琳. 基于非线性模态的复杂系统动力学特性分析方法[J]. 北京航空航天大学学报, 2019, 45(7): 1337-1348.
[2]	虞江航, 徐军, 黄雨可. 一类反馈型非线性系统的跟踪控制[J]. 北京航空航天大学学报, 2019, 45(7): 1444-1450.
[3]	曹慧, 段富海, 江秀红. 基于UGF-GO法的EWIS退化系统可靠性分析[J]. 北京航空航天大学学报, 2019, 45(6): 1153-1161.
[4]	杨斌, 史开元, 袁廷璧, 肖德铭, 王侃, 李振海. 金属材料微裂纹取向与超声波和频非线性效应[J]. 北京航空航天大学学报, 2019, 45(4): 695-704.
[5]	柴睿, 谭申刚, 黄国宁. 考虑几何非线性的气动弹性模型缩比方法[J]. 北京航空航天大学学报, 2019, 45(4): 743-751.
[6]	武梅丽文, 陈铭, 王放. 悬停状态下小型无人直升机飞行动力学模型辨识[J]. 北京航空航天大学学报, 2019, 45(3): 546-559.
[7]	宋鑫, 郑冠男, 杨国伟, 姜倩. 几何不确定性区间分析及鲁棒气动优化设计[J]. 北京航空航天大学学报, 2019, 45(11): 2217-2227.
[8]	叶威, 马齐爽, 徐萍, 张珀铭. 开关磁阻电机矩角特性模型非线性拟合方法[J]. 北京航空航天大学学报, 2019, 45(1): 83-92.
[9]	王春雷, 赵琦, 秦孝丽, 冯文全. 基于改进相关向量机的锂电池寿命预测方法[J]. 北京航空航天大学学报, 2018, 44(9): 1998-2003.
[10]	孙健, 付永领, 何杰, 谭林, 李胜广, 许文鹏. 基于非线性规划的室内TOA测距值优化方法[J]. 北京航空航天大学学报, 2018, 44(8): 1636-1642.
[11]	吴靖, 胡国才, 刘湘一. 适于时变幅值分析的直升机黏弹减摆器模型[J]. 北京航空航天大学学报, 2018, 44(8): 1665-1671.
[12]	袁庆洋, 叶建华, 李晓钢. BLDC电机温度退化多段Wiener过程建模[J]. 北京航空航天大学学报, 2018, 44(7): 1514-1519.
[13]	贾伟州, 彭靖波, 谢寿生, 刘云龙, 李腾辉, 何大伟. 非线性多项式模型结构与参数一体化辨识[J]. 北京航空航天大学学报, 2018, 44(6): 1303-1311.
[14]	高冬, 宋智斌, 赵亚茹. 干扰观测器在一种非线性刚度驱动器中的应用[J]. 北京航空航天大学学报, 2018, 44(6): 1328-1336.
[15]	吴欢, 焦健, 赵廷弟. 一种考虑共因失效的PMS可靠性建模分析方法[J]. 北京航空航天大学学报, 2018, 44(5): 1088-1094.