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首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs
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Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs

机译：新型稀疏诱导双卡尔曼滤波器及其在跟踪时变空间稀疏结构刚度变化和输入的应用

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摘要

While the theory of Bayesian system identification provides a probabilistic means for reliably and robustly inferring models of a dynamic system and their parameters based on measured dynamic response, exploiting sparseness during online tracking of changing model parameters is not well understood. The focus in this study is to implement the dual Kalman filter for real-time Bayesian sequential state and parameter identification based on noisy sensor signals while incorporating sparse Bayesian learning to impose sparse model parameter changes from their initial reference values. We also want out model to be able to capture the evolution of sparse model parameter changes between two successive time instants. To this end, we present a hierarchical Bayesian model for tracking the joint posterior distribution of the state and model parameter vectors for a monitored dynamical system, where the two afore-mentioned sparseness constraints (sparse changes from reference values and with time) are also effectively incorporated for each time. We show our stochastic model of the structural dynamical system can be represented as a coupled conditionally-linear Gaussian state space model for the state and model parameter vectors, leading to some interesting analytical properties of the method that allow quantities of interest to be calculated in real time by using Kalman filtering equations. The parameters for the measurement and state prediction errors are learned solely from the available data up to the current time and so our method resolves the well-known instability problem in Kalman filtering due to arbitrary assignment of the error-distribution parameters. Finally, two illustrative applications are presented, one for identification of stiffness degradat ion and the other for input time-history identification where both are based on noisy dynamic response measurements from a structure. (C) 2020 Elsevier B.V. All rights reserved.
机译：虽然贝叶斯系统识别理论提供了一种概率的方法，用于基于测量的动态响应的动态系统及其参数的可靠和鲁棒地推断的概率方法，并且在在线跟踪期间在在线跟踪期间的改变模型参数的稀疏性是不太了解的。本研究的焦点是基于嘈杂的传感器信号实现实时贝叶斯顺序状态和参数识别的双卡尔曼滤波器，同时包含稀疏贝叶斯学习，从而从它们的初始参考值施加稀疏模型参数变化。我们还希望模型能够捕获两个连续时间瞬间之间稀疏模型参数变化的演变。为此，我们介绍了一种分层贝叶斯模型，用于跟踪状态和模型参数向量的接头后部分布，用于监测的动态系统，其中两个上述稀疏约束（来自参考值和时间的稀疏变化）也是有效的每次都注册。我们表明我们的结构动态系统的随机模型可以表示为状态和模型参数向量的耦合条件 - 线性高斯状态空间模型，从而导致允许在真实中计算的方法的一些有趣的分析特性使用卡尔曼滤波方程的时间。测量和状态预测错误的参数仅仅从可用数据到当前时间的方式学习，因此我们的方法由于误差分配参数的任意分配而解析了卡尔曼滤波中的众所周知的不稳定性问题。最后，提出了两个说明性应用，一个用于识别刚度DegRadat离子，另一个用于输入时间历史标识，其中两者基于来自结构的噪声动态响应测量。 （c）2020 Elsevier B.v.保留所有权利。

著录项

  • 来源
    《Computer Methods in Applied Mechanics and Engineering》 |2020年第2期|113411.1-113411.34|共34页
  • 作者

    Huang Yong; Yu Jianqi; Beck James L.; Zhu Huiping; Li Hui;

  • 作者单位

    Harbin Inst Technol Sch Civil Engn Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Peoples R China|Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China;

    Harbin Inst Technol Sch Civil Engn Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Peoples R China|Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China;

    CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA;

    Harbin Inst Technol Sch Civil Engn Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Peoples R China|Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China;

    Harbin Inst Technol Sch Civil Engn Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disast Harbin 150090 Peoples R China|Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bayesian system identification; Sparse Bayesian learning; Kalman filter; Online model error and noise identification; Dual sequential state and parameter estimation; Input time-history identification;

    机译：贝叶斯系统识别;稀疏贝叶斯学习;卡尔曼滤波器;在线模型错误和噪声识别;双顺序状态和参数估计;输入时间历史识别;

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