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首页> 外文期刊>Mechanical systems and signal processing >On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification
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On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification

机译：高斯过程潜力模型在联合输入状态参数估计中的应用：以贝叶斯辨识为目的

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摘要

The problem of identifying dynamic structural systems is of key interest to modern engineering practice and is often a first step in an analysis chain, such as validation of computer models or structural health monitoring. While this topic has been well covered for tests conducted in a laboratory setting, identification of full-scale structures in place remains challenging. Additionally, during in service assessment, it is often not possible to measure the loading that a given structure is subjected to; this could be due to practical limitations or cost. Current solutions to this problem revolve around assumptions regarding the nature of the load a structure is subject to; almost exclusively this is assumed to be a white Gaussian noise. However, in many cases this assumption is insufficient and can lead to biased results in system identification. This current work presents a model which attempts the system identification task (in terms of the parametric estimation) in conjunction with estimation of the inputs to the system and the latent states—the displacements and velocities of the system. Within this paper, a Bayesian framework is presented for rigorous uncertainty quantification over both the system parameters and the unknown input signal. A Gaussian process latent force model allows a flexible Bayesian prior to be placed over the unknown forcing signal, which in conjunction with the state-space representation, allows fully Bayesian inference over the complete dynamic system and the unknown inputs.
机译：识别动态结构系统的问题是现代工程实践的关键问题，并且通常是分析链中的第一步，例如计算机模型或结构健康监控的验证。尽管在实验室环境中进行的测试已经很好地涵盖了该主题，但要确定合适的全尺寸结构仍然具有挑战性。另外，在使用中评估期间，通常不可能测量给定结构所承受的载荷;这可能是由于实际限制或成本。当前该问题的解决方案围绕着关于结构所承受载荷的性质的假设。几乎完全假定这是白高斯噪声。但是，在许多情况下，这种假设是不够的，并且可能导致系统识别结果出现偏差。当前工作提供了一个模型，该模型尝试结合系统输入和潜在状态（系统的位移和速度）的估计来尝试系统识别任务（就参数估计而言）。在本文中，提出了贝叶斯框架，用于对系统参数和未知输入信号进行严格的不确定性量化。高斯过程潜力模型允许将灵活的贝叶斯先于未知强迫信号放置，再结合状态空间表示，就可以对整个动态系统和未知输入进行完全贝叶斯推断。

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