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 time-series sensor data. Potential applications encompass predictive maintenance and asset management in industries embracing Industry 4.0 and 5.0 paradigms.