Deep Variational Bayesian Approach for Sensor‐Driven Modeling and Monitoring of Dynamic Degrading Systems

ABSTRACT This article introduces a deep state‐space modeling (DSSM) framework tailored for monitoring complex and dynamic deteriorating systems operating under varying operating conditions and sensor‐based condition monitoring. By integrating stochastic recurrent neural networks (RNNs) with a generative model in a variational autoencoder (VAE) form, the proposed framework effectively approximates the complex behaviors inherent in degrading systems with latent states and captures long‐term dependencies and latent dynamics without relying on unrealistic distributional and parametric assumptions. The framework leverages RNNs to model temporal dependencies and VAE to model robust probabilistic inference, enabling accurate latent state estimation and time to event (TE) prediction. Moreover, its flexibility extends to accommodating both continuous and discrete latent states, enriching the representation of underlying data dynamics. By performing joint inference and learning, utilizing VAE for system dynamics modeling offers significant advantages over traditional state‐space models (SSMs), which require high computational resources and tuning. We have tested this framework using both simulation and real‐world datasets. Also, a case study on a wind turbine dataset demonstrates the effectiveness of the proposed framework in early fault detection.