Predictive Maintenance

Predictive maintenance uses data-driven methods to anticipate equipment failures, optimizing maintenance schedules and minimizing downtime. Current research emphasizes the application of machine learning, particularly deep learning architectures like transformers, recurrent neural networks (RNNs, including LSTMs and GRUs), and ensemble methods, often coupled with survival analysis techniques to handle censored data and improve RUL (Remaining Useful Life) prediction. This field is crucial for enhancing industrial efficiency, safety, and sustainability across diverse sectors, with ongoing efforts focused on improving model explainability, robustness against adversarial attacks, and handling data scarcity through techniques like data augmentation.