Useful Life

Remaining Useful Life (RUL) prediction aims to estimate the time until a system or component fails, enabling proactive maintenance and preventing costly downtime. Current research heavily emphasizes data-driven approaches, employing deep learning architectures like recurrent neural networks (RNNs), convolutional neural networks (CNNs), graph neural networks (GNNs), and transformers, often incorporating attention mechanisms and physics-informed models to improve accuracy and interpretability. These advancements are significantly impacting various industries, from predictive maintenance in manufacturing and aviation to optimizing the lifespan of batteries in electric vehicles and energy storage systems. The focus is on handling complex, high-dimensional sensor data, addressing data scarcity and variability in operating conditions, and quantifying prediction uncertainty.