Variational Recurrent Neural Networks (VRNN) for RUL Estimation

This study introduces an innovative deep learning
approach for predicting the remaining useful life (RUL) of
industrial machinery. Accurate RUL forecasts play a crucial role
in enabling proactive maintenance and enhancing operational
efficiency within the contexts of Industry 4.0 and 5.0. However,
current data-driven models face challenges when dealing with
complex, high-dimensional time-series sensor data.
To address these obstacles, we present a framework for predicting RUL of industrial equipment, employing variational recurrent neural networks (VRNNs). By harnessing the expressive
power of recurrent neural networks and variational inference,
VRNNs demonstrate proficiency in modeling temporal relationships and managing intricate, high-dimensional time-series
sensor data. This deep learning model represents a significant
advancement in data-driven prognostics, providing more robust
and interpretable RUL forecasts derived from intricate timeseries sensor data. Potential applications encompass predictive
maintenance and asset management in industries embracing
Industry 4.0 and 5.0 paradigms.