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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 out-puts), 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 dis-cussed. Numerical experiments are provided to demonstrate the application of the proposed framework for degradation system modeling and monitoring. 

In this work, we present a new approach for latent system dynamics and remaining useful life (RUL) estimation of complex degrading systems using generative modeling and reinforcement learning. The main contributions of the proposed method are two-fold. First, we show how a deep generative model can approximate the functionality of high-fidelity simulators and, thus, is able to substitute expensive and complex physics-based models with data-driven surrogate ones. In other words, we can use the generative model in lieu of the actual system as a surrogate model of the system. Furthermore, we show how to use such surrogate models for predictive analytics. Our method follows two main steps. First, we use a deep variational autoencoder (VAE) to learn the distribution over the latent state-space that characterizes the dynamics of the system under monitoring. After model training, the probabilistic VAE decoder becomes the surrogate system model. Then, we develop a scalable reinforcement learning framework using the decoder as the environment, to train an agent for identifying adequate approximate values of the latent dynamics, as well as the RUL.