Failure Prediction

Failure prediction research aims to anticipate malfunctions across diverse systems, from computer hardware and industrial equipment to robots and cloud infrastructure, ultimately improving reliability and efficiency. Current efforts focus on developing robust predictive models using machine learning techniques, including transformers, neural networks, and ensemble methods, often incorporating data imputation strategies to handle incomplete datasets. These advancements are crucial for optimizing maintenance schedules, enhancing safety in critical applications, and reducing downtime and associated costs across various industries.