一种基于数据驱动和贝叶斯理论的机械系统剩余寿命预测方法

doi:10.3901/JME.2018.12.115

机械工程学报 ›› 2018, Vol. 54 ›› Issue (12): 115-124.doi: 10.3901/JME.2018.12.115

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一种基于数据驱动和贝叶斯理论的机械系统剩余寿命预测方法

赵申坤, 姜潮, 龙湘云

湖南大学特种装备先进设计技术与仿真教育部重点实验室长沙 410082

收稿日期:2017-07-20 修回日期:2017-10-30 出版日期:2018-06-20 发布日期:2018-06-20
通讯作者: 姜潮(通信作者),男,1978年出生,博士,教授,博士研究生导师。主要研究方向为现代设计技术,机械可靠性。E-mail:jiangc@hnu.edu.cn
作者简介:赵申坤,男,1991年出生,博士研究生。主要研究方向为故障预测与健康管理。E-mail:shenkun_zhao@hnu.edu.cn
基金资助:
国家杰出青年科学基金（51725502）、国家自然科学基金重大项目（51490662）和国家重点研发计划项目（2016YFD0701105）资助项目。

Remaining Useful Life Estimation of Mechanical Systems Based on the Data-driven Method and Bayesian Theory

ZHAO Shenkun, JIANG Chao, LONG Xiangyun

Key Laboratory of Advanced Design and Simulation Techniques for Special Equipment of Ministry of Education, Hunan University, Changsha 410082

Received:2017-07-20 Revised:2017-10-30 Online:2018-06-20 Published:2018-06-20

摘要/Abstract

摘要： 针对复杂机械系统剩余寿命预测问题，提出一种基于数据驱动方法和贝叶斯理论的机械系统剩余寿命预测方法。该方法基于数据驱动方法，对相同或相似系统的历史状态监测数据进行融合，并建立表征系统退化程度的健康指示量和预测剩余寿命的状态模型；基于贝叶斯理论，建立状态模型参数的贝叶斯模型；在此基础上，基于待估系统的实时状态监测数据和贝叶斯模型，利用马尔科夫链蒙特卡洛方法来更新模型参数并预测待估系统的剩余寿命；通过一个航空发动机的预测问题，来说明该方法的有效性。

关键词: 贝叶斯模型, 剩余寿命预测, 数据驱动

Abstract: A novel remaining useful life(RUL) estimation method is proposed based on the data-driven method and Bayesian theory for the remaining useful life estimation of complex mechanical systems. Firstly, the condition monitoring data of same or similar systems are fused to form the Health Index indicating the degradation degree of systems and the state model by the data-driven method. Then, a Bayesian model of the state model parameters is built on Bayesian theory. With the on-line condition monitoring data of system to be estimated and the Bayesian model, the model parameters are updated by Markov Chain Monte Carlo (MCMC) and the RUL of system is estimated. At last, a turbofan engine case is used to show the effectiveness of the present method.

Key words: Bayesian model, data-driven, remaining useful life

中图分类号:

TG156

赵申坤, 姜潮, 龙湘云. 一种基于数据驱动和贝叶斯理论的机械系统剩余寿命预测方法[J]. 机械工程学报, 2018, 54(12): 115-124.

ZHAO Shenkun, JIANG Chao, LONG Xiangyun. Remaining Useful Life Estimation of Mechanical Systems Based on the Data-driven Method and Bayesian Theory[J]. Journal of Mechanical Engineering, 2018, 54(12): 115-124.

参考文献

[1] AYE S A, HEYNS P S. An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission[J]. Mechanical Systems and Signal Processing, 2017, 84, Part A:485-498.
[2] 雷亚国, 陈吴, 李乃鹏, 等. 自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用[J]. 机械工程学报, 2016, 52(1):87-93. LEI Yaguo, CHEN Wu, LI Naipeng, et al. A Relevance vector machine prediction method based on adaptive multi-kernel combination and its application to remaining useful life prediction of machinery[J]. Journal of Mechanical Engineering, 2016, 52(1):87-93.
[3] LEE J, WU Fangji, ZHAO Wenyu, et al. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications[J]. Mechanical Systems and Signal Processing, 2014, 42(1-2):314-334.
[4] ZHOU Yapeng, HUANG Miaohua, CHEN Yupu, et al. A novel health indicator for on-line lithium-ion batteries remaining useful life prediction[J]. Journal of Power Sources, 2016, 321:1-10.
[5] LU Languang, HAN Xuebing, LI Jianqiu, et al. A review on the key issues for lithium-ion battery management in electric vehicles[J]. Journal of Power Sources, 2013, 226:272-288.
[6] LING Y, MAHADEVAN S. Integration of structural health monitoring and fatigue damage prognosis[J]. Mechanical Systems and Signal Processing, 2012, 28:89-104.
[7] ESPERON-MIGUEZ M, JOHN P, JENNIONS I K. A review of integrated vehicle health management tools for legacy platforms:Challenges and opportunities[J]. Progress in Aerospace Sciences, 2013, 56:19-34.
[8] JOHNSON S B, GORMLEY T, KESSLER S, et al. System health management:with aerospace applications[M]. John Wiley & Sons, 2011.
[9] AN D, KIM N H, CHOI J H. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews[J]. Reliability Engineering & System Safety, 2015, 133:223-236.
[10] BARALDI P, CADINI F, MANGILI F, et al. Model-based and data-driven prognostics under different available information[J]. Probabilistic Engineering Mechanics, 2013, 32:66-79.
[11] HENG A, ZHANG S, TAN A C C, et al. Rotating machinery prognostics:State of the art, challenges and opportunities[J]. Mechanical Systems and Signal Processing, 2009, 23(3):724-739.
[12] SI Xiaosheng, WANG Wenbin, HU Changhua, et al. Remaining useful life estimation:A review on the statistical data driven approaches[J]. European Journal of Operational Research, 2011, 213(1):1-14.
[13] LIAO L, KÖTTIG F. A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction[J]. Applied Soft Computing, 2016, 44:191-199.
[14] YOU G W, PARK S, OH D. Real-time state-of-health estimation for electric vehicle batteries:A data-driven approach[J]. Applied Energy, 2016, 176:92-103.
[15] XI Z, JING R, WANG P, et al. A copula-based sampling method for data-driven prognostics[J]. Reliability Engineering & System Safety, 2014, 132:72-82.
[16] SON K L, FOULADIRAD M, BARROS A. Remaining useful lifetime estimation and noisy gamma deterioration process[J]. Reliability Engineering & System Safety, 2016, 149:76-87.
[17] 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.
[18] HU C, YOUN B D, WANG P, et al. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life[J]. Reliability Engineering & System Safety, 2012, 103:120-135.
[19] SON K L, FOULADIRAD M, BARROS A, et al. Remaining useful life estimation based on stochastic deterioration models:A comparative study[J]. Reliability Engineering & System Safety, 2013, 112:165-175.
[20] WANG T, Yu Jianbo, SIEGEL D, et al. A similarity based prognostics approach for Remaining Useful Life estimation of engineered systems[C]//Prognostics and Health Management, 2008. PHM 2008. International Conference on, 2008:1-6.
[21] WALTER G, ASLETT L J M, COOLEN F P A. Bayesian nonparametric system reliability using sets of priors[J]. International Journal of Approximate Reasoning, 2017, 80:67-88.
[22] RAUSAND M, HØYLAND A. System reliability theory:models, statistical methods, and applications[M]. John Wiley & Sons, 2004.
[23] FLAGE R, COIT D W, LUXHØJ J T, et al. Safety constraints applied to an adaptive Bayesian condition based maintenance optimization model[J]. Reliability Engineering & System Safety, 2012, 102(2):16-26.
[24] MOSALLAM A, MEDJAHER K, ZERHOUNI N. Data driven prognostic method based on Bayesian approaches for direct remaining useful life prediction[J]. Journal of Intelligent Manufacturing, 2016, 27(5):1037-1048.
[25] HAMADA M S, WILSON A, REESE C S, et al. Bayesian reliability[M]. Berlin:Springer Science & Business Media, 2008.
[26] HE W, WILLIARD N, OSTERMAN M, et al. Prognostics of lithium-ion batteries based on Dempster Shafer theory and the Bayesian Monte Carlo method[J]. Journal of Power Sources, 2011, 196(23):10314-10321.
[27] 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.
[28] GEBRAEEL N, PAN J. Prognostic degradation models for computing and updating residual life distributions in a time-varying environment[J]. IEEE Transactions on Reliability, 2008, 57(4):539-550.
[29] GEBRAEEL N, ELWANY A, PAN J. Residual life predictions in the absence of prior degradation knowledge[J]. IEEE Transactions on Reliability, 2009, 58(1):106-117.
[30] ZAIDAN M A, MILLS A R, HARRISON R F, et al. Gas turbine engine prognostics using Bayesian hierarchical models:A variational approach[J]. Mechanical Systems and Signal Processing, 2016, 70-71:120-140.
[31] SUN Jianzhong, ZUO Hongfu, WANG Wenbin, et al. Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model[J]. Mechanical Systems and Signal Processing, 2014, 45(2):396-407.
[32] ZÁRATE B A, CAICEDO J M, YU J, et al. Bayesian model updating and prognosis of fatigue crack growth[J]. Engineering Structures, 2012, 45:53-61.
[33] WANG X, BALAKRISHNAN N, GUO B. Residual life estimation based on a generalized Wiener degradation process[J]. Reliability Engineering & System Safety, 2014, 124:13-23.
[34] SAXENA A, GOEBEL K, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]//Prognostics and Health Management, 2008. PHM 2008. International Conference on, 2008:1-9.
[35] SAXENA A, GOEBEL K.Turbofan Engine Degradation Simulation Data Set[DB/OL]. Moffett Field, CA:NASA Ames Research Center, 2008[2016-05-07]. http://ti.arc.nasa.gov/project/prognostic-data-repository.
[36] BENKEDJOUH T, MEDJAHER K, ZERHOUNI N, et al. Fault prognostic of bearings by using support vector data description[C]//Prognostics and Health Management (PHM), 2012 IEEE Conference on, 2012:1-7.
[37] MOGHADDASS R, ZUO M J. An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process[J]. Reliability Engineering & System Safety, 2014, 124:92-104.
[38] FOSTER P. Exploring multivariate data using directions of high density[J]. Statistics and Computing, 1998, 8(4):347-355.
[39] ABDI H, WILLIAMS L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews:Computational Statistics, 2010, 2(4):433-459.
[40] TRAN V T, THOM PHAM H, YANG B S, et al. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J]. Mechanical Systems and Signal Processing, 2012, 32:320-330.

一种基于数据驱动和贝叶斯理论的机械系统剩余寿命预测方法

Remaining Useful Life Estimation of Mechanical Systems Based on the Data-driven Method and Bayesian Theory

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