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Optimization of nonlinear, non-Gaussian Bayesian filtering for diagnosis and prognosis of monotonic degradation processes

机译：非线性，非高斯贝叶斯滤波的优化，用于诊断和预测单调降解过程

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摘要

The present work critically analyzes the probabilistic definition of dynamic state-space models subject to Bayesian filters used for monitoring and predicting monotonic degradation processes. The study focuses on the selection of the random process, often called process noise, which is a key perturbation source in the evolution equation of particle filtering. Despite the large number of applications of particle filtering predicting structural degradation, the adequacy of the picked process noise has not been investigated. This paper reviews existing process noise models that are typically embedded in particle filters dedicated to monitoring and predicting structural damage caused by fatigue, which is monotonic in nature. The analysis emphasizes that existing formulations of the process noise can jeopardize the performance of the filter in terms of state estimation and remaining life prediction (i.e., damage prognosis). This paper subsequently proposes an optimal and unbiased process noise model and a list of requirements that the stochastic model must satisfy to guarantee high prognostic performance. These requirements are useful for future and further implementations of particle filtering for monotonic system dynamics. The validity of the new process noise formulation is assessed against experimental fatigue crack growth data from a full-scale aeronautical structure using dedicated performance metrics.
机译：本工作批判性地分析了动态状态空间模型的概率定义，该模型受用于监视和预测单调降解过程的贝叶斯滤波器的影响。该研究的重点是随机过程的选择，通常称为过程噪声，它是粒子过滤演化方程中的关键扰动源。尽管粒子过滤的大量应用可预测结构退化，但尚未研究拾取的过程噪声是否足够。本文回顾了通常存在于颗粒过滤器中的现有过程噪声模型，这些模型专门用于监视和预测由疲劳引起的结构损坏，疲劳本质上是单调的。分析强调，过程噪声的现有公式可能会在状态估计和剩余寿命预测（即损坏预后）方面危及滤波器的性能。随后，本文提出了一种最佳且无偏的过程噪声模型，并提出了随机模型必须满足的条件列表，以保证较高的预后性能。这些要求对于单调系统动力学的粒子滤波的未来和进一步实现很有用。使用专用的性能指标，针对来自完整航空结构的实验疲劳裂纹扩展数据，评估了新工艺噪声公式的有效性。

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