EI、Scopus 收录
中文核心期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

预测结构性能退化的混合粒子滤波方法

关雪雪 陈建桥 郑瑶辰 张晓生

关雪雪, 陈建桥, 郑瑶辰, 张晓生. 预测结构性能退化的混合粒子滤波方法[J]. 力学学报, 2018, 50(3): 677-687. doi: 10.6052/0459-1879-18-014
引用本文: 关雪雪, 陈建桥, 郑瑶辰, 张晓生. 预测结构性能退化的混合粒子滤波方法[J]. 力学学报, 2018, 50(3): 677-687. doi: 10.6052/0459-1879-18-014
Guan Xuexue, Chen Jianqiao, Zheng Yaochen, Zhang Xiaosheng. A COMBINED PARTICLE FILTER METHOD FOR PREDICTING STRUCTURAL PERFORMANCE DEGRADATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2018, 50(3): 677-687. doi: 10.6052/0459-1879-18-014
Citation: Guan Xuexue, Chen Jianqiao, Zheng Yaochen, Zhang Xiaosheng. A COMBINED PARTICLE FILTER METHOD FOR PREDICTING STRUCTURAL PERFORMANCE DEGRADATION[J]. Chinese Journal of Theoretical and Applied Mechanics, 2018, 50(3): 677-687. doi: 10.6052/0459-1879-18-014

预测结构性能退化的混合粒子滤波方法

doi: 10.6052/0459-1879-18-014
基金项目: 国家自然科学基金资助项目(11572134).
详细信息
    作者简介:

    通讯作者:陈建桥,教授,主要研究方向:结构可靠性分析等. E-mail:jqchen@mail.hust.edu.cn;郑瑶辰,博士研究生,主要研究方向:结构优化设计. E-mail:zyc276565153@126.com

    通讯作者:

    陈建桥,郑瑶辰

    陈建桥,郑瑶辰

  • 中图分类号: O346.2;

A COMBINED PARTICLE FILTER METHOD FOR PREDICTING STRUCTURAL PERFORMANCE DEGRADATION

  • 摘要: 由于载荷,环境以及材料内部因素的作用,结构的性能一般随时间而逐渐退化. 为了评估结构服役期间的状态,常采用随机变量模型来描述结构性能的退化规律. 即,采用含不确定性模型参数的物理模型来逼近结构响应特性. 利用同类型结构的先知数据集信息可确定模型参数的先验分布. 结合结构服役期间的检测信息和贝叶斯原理,对模型参数进行更新,从而提高物理模型的准确性. 本文提出一种混合粒子滤波方法(particle filter-differential evolution adaptive Metropolis,PF-DREAM)用于模型更新,即:在确定参数先验分布时,采用证据理论(Dempster-shafer theory, DST)初始化模型参数;结合差分进化自适应 Metropolis 算法(differential evolution adaptive Metropolis, DREAM)和粒子滤波(particle filter, PF)算法,来计算更新公式中的复杂的高维积分. 相比于传统的 PF 算法,混合 PF-DREAM 方法可以有效提高样本粒子的多样性,解决重采样算法中粒子多样性匮乏的问题,从而得到更加合理的物理模型. 为了证明该方法的有效性,将提出的方法分别应用于电池性能退化和裂纹扩展规律预测. 算例表明采用本文提出的模型参数确定方法,使得物理模型更加合理,性能预测更加准确. 用于更新的数据越多,模型参数的分散性越小. 本文方法应用于高维问题或隐式函数问题时,计算原理和步骤不发生改变,但函数评价次数和计算时间会随之增大.

     

  • [1] Tsai CC. Mis-specification analyses of gamma and Wiener degradation processes.Journal of Statistical Planning & Inference, 2011, 141(12): 3725-3735
    [2] Dan MF, Kallen MJ, Noortwijk JMV. Probabilistic models for life-cycle performance of deteriorating structures: Review and future directions.Progress in Structural Engineering & Materials, 2004, 6(4): 197-212
    [3] 刘海波, 姜潮, 郑静等. 含概率与区间混合不确定性的系统可靠性分析方法. 力学学报, 2017, 49(2): 456-466
    [3] (Liu Haibo, Jiang Chao, Zheng Jing, et al. A system reliability analysis method for structures with probability and interval mixed uncertainty.Chinese Journal of Theoretical and Applied Mechanics, 2017, 49(2): 456-466 (in Chinese))
    [4] Ge R, Chen J, Wei J. Reliability-based design of composites under the mixed uncertainties and the optimization algorithm.Acta Mechanica Solida Sinica, 2008, 21(1): 19-27
    [5] 李帅兵, 杨睿, 罗喜胜等. 气流作用下同轴带电射流的不稳定性研究. 力学学报, 2017, 49(5): 997-1007
    [5] (Li Shuaibing, Yang Rui, Luo Xisheng, et al. Instability study of an electrified coaxial jet in a coflowing gas stream.Chinese Journal of Theoretical and Applied Mechanics, 2017, 49(5): 997-1007 (in Chinese))
    [6] 周春晓,汪锐琼,聂肇坤等. 基于最大熵方法的水下航行体结构动力响应概率建模. 力学学报, 2018, 50(1): 114-123
    [6] (Zhou Chunxiao,Wang Ruiqiong, Nie Zhaokun, et al. Probabilistic modelling of dynamic response of underwater vehicle structure via maximum entropy method.Chinese Journal of Theoretical and Applied Mechanics, 2018, 50(1): 114-123 (in Chinese))
    [7] Huang X, Chen J. Time-dependent reliability model of deteriorating structures based on stochastic processes and Bayesian inference methods.Journal of Engineering Mechanics, 2015, 141(3): 04014123
    [8] Guan X, He J, Jha R, et al. An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations.Reliability Engineering & System Safety, 2012, 97(1): 1-13
    [9] He W, Williard N, Osterman M, et al. Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method.Journal of Power Sources, 2011, 196(23): 10314-10321
    [10] Rebba R, Mahadevan S. Computational methods for model reliability assessment.Reliability Engineering & System Safety, 2008, 93(8): 1197-1207
    [11] Luke T, Joseph BK. Accurate Approximations for posterior moments and marginal densities.Journal of the American Statistical Association, 1986, 81(393): 82-86
    [12] 王宇. 贝叶斯参数更新在可靠性分析中的应用. [硕士论文]. 南京:南京航空航天大学, 2014
    [12] (Wang Yu. The application of Bayesian updating in reliability analysis. [Master Thesis]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014 (in Chinese))
    [13] Cappé O, Moulines E, Rydén T. Inference in Hidden Markov Models. Springer, 2005: 574-575
    [14] Pitt MK, Silva RDS, Giordani P, et al. On some properties of Markov chain Monte Carlo simulation methods based on the particle filter.Journal of Econometrics, 2012, 171(2): 134-151
    [15] Fernándezvillaverde J, Rubioramrez JF. Estimating macroeconomic models: A likelihood approach.Review of Economic Studies, 2010, 74(4): 1059-1087
    [16] Christophe A, Arnaud D, Roman H. Particle Markov chain Monte Carlo methods.Journal of the Royal Statistical Society, 2010, 72(3): 269-342
    [17] Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering.Statistics & Computing, 2000, 10(3):197-208
    [18] An D, Choi JH, Kim NH. Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab.Reliability Engineering & System Safety, 2013, 115(1): 161-169
    [19] 王鑫, 胡昌华, 暴飞虎. 基于贝叶斯原理的粒子滤波算法. 弹箭与制导学报, 2006, 26(s5): 269-271
    [19] (Wang Xin, Hu Changhua, Bao Feihu. Particle filtering method based on Bayesian theorem.Journal of Projectiles, Roktets, Missiles and Guidance, 2006, 26(s5): 269-271 (in Chinese))
    [20] Chen J, Yuan S, Qiu L, et al. On-line prognosis of fatigue crack propagation based on Gaussian weight-mixture proposal particle filter.Ultrasonics, 2017, 82: 134
    [21] Gordon NJ, Salmond DJ, Smith AFM. Novel approach to nonlinear/non-Gaussian Bayesian state estimation.IEE Proceedings F-Radar and Signal Processing, 2002, 140(2): 107-113
    [22] Djurić PM, Kotecha JH, Zhang J, et al. Particle filtering.Signal Processing Magazine IEEE, 2003, 20(5): 19-38
    [23] Zio E, Peloni G. Particle filtering prognostic estimation of the remaining useful life of nonlinear components.Reliability Engineering & System Safety, 2011, 96(3): 403-409
    [24] Bi H, Ma J, Wang F. An improved particle filter algorithm based on Ensemble Kalman filter and Markov Chain Monte Carlo method.IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(2): 447-459
    [25] Vrugt JA, Braak CJFT, Clark MP, et al. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation.Water Resources Research, 2008, 44(12): 5121-5127
    [26] Vrugt JA, Braak CJFT, Diks CGH, et al. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling.International Journal of Nonlinear Sciences & Numerical Simulation, 2009, 10(3): 273-290
    [27] Laloy E, Vrugt JA. High-dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high-performance computing.Water Resources Research, 2012, 50(3): 182-205
    [28] Vrugt J A. Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation.Environmental Modelling & Software, 2016, 75: 273-316
    [29] Wentworth MT, Smith RC, Williams B. Bayesian model calibration and uncertainty quantification for an HIV model using adaptive Metropolis algorithms.Inverse Problems in Science & Engineering, 2017, 26(2): 233-256
    [30] Post H, Vrugt JA, Fox A, et al. Estimation of community land model parameters for an improved assessment of net carbon fluxes at European sites.Journal of Geophysical Research Biogeosciences, 2017, 122(3): 661-689
    [31] Liang S, Jia H, Xu C, et al. A Bayesian approach for evaluation of the effect of water quality model parameter uncertainty on TMDLs: A case study of Miyun reservoir. Science of the Total Environment, 2016, s560-561: 44-54
    [32] Wu WF, Ni CC. A study of stochastic fatigue crack growth modeling through experimental data.Probabilistic Engineering Mechanics, 2003, 18(2): 107-118
    [33] Wu WF, Ni CC. Statistical aspects of some fatigue crack growth data.Engineering Fracture Mechanics, 2007, 74(18): 2952-2963
  • 加载中
计量
  • 文章访问数:  1026
  • HTML全文浏览量:  135
  • PDF下载量:  298
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-01-08
  • 刊出日期:  2018-05-18

目录

    /

    返回文章
    返回