State-space modeling for degrading systems with stochastic neural networks and dynamic Bayesian layers
State-space modeling for degrading systems with stochastic neural networks and dynamic Bayesian layers
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DOI：
10.1080/24725854.2023.2185323
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发表时间：
2023-02
影响因子：
2.6
通讯作者：
Md Tanzin Farhat;R. Moghaddass
中科院分区：
文献类型：
--
作者：
Md Tanzin Farhat;R. Moghaddass
摘要：为了监测退化系统随时间的动态行为，引入了一种灵活的分层离散时间状态空间模型（SSM），该模型可以在数学上表征退化系统的潜在状态（离散、连续或混合）的随机演化、从状态监测源（例如具有混合类型输出的传感器）收集的动态测量以及故障过程。这种灵活的 SSM 受到贝叶斯分层建模和循环神经网络的启发，无需强加有关系统动力学及其变量的随机结构的先验知识。退化系统的时间行为以及相应系统动力学的变量之间的关系由随机神经网络完全表征，而无需定义确定性变量和随机变量之间的参数关系/分布。引入基于贝叶斯过滤的学习方法来用历史数据训练所提出的框架的结构。此外，还讨论了利用所提出的框架来推断和预测潜在状态和传感器输出的步骤。提供了数值实验来演示所提出的退化系统建模和监测框架的应用。
Abstract To monitor the dynamic behavior of degrading systems over time, a flexible hierarchical discrete-time state-space model (SSM) is introduced that can mathematically characterize the stochastic evolution of the latent states (discrete, continuous, or hybrid) of degrading systems, dynamic measurements collected from condition monitoring sources (e.g., sensors with mixed-type outputs), and the failure process. This flexible SSM is inspired by Bayesian hierarchical modeling and recurrent neural networks without imposing prior knowledge regarding the stochastic structure of the system dynamics and its variables. The temporal behavior of degrading systems and the relationship between variables of the corresponding system dynamics are fully characterized by stochastic neural networks without having to define parametric relationships/distributions between deterministic and stochastic variables. A Bayesian filtering-based learning method is introduced to train the structure of the proposed framework with historical data. Also, the steps to utilize the proposed framework for inference and prediction of the latent states and sensor outputs are discussed. Numerical experiments are provided to demonstrate the application of the proposed framework for degradation system modeling and monitoring.