Data Driven Prognostic

Data-driven prognostics leverages machine learning to predict the remaining useful life or likelihood of failure in various systems, from industrial equipment to human tissue samples. Current research emphasizes improving model accuracy and robustness through techniques like advanced feature extraction (e.g., using deep learning architectures such as ResNet and incorporating time-frequency analysis), handling data scarcity and imbalance via domain expertise integration and synthetic data generation, and incorporating uncertainty quantification for more reliable predictions. This field is crucial for optimizing maintenance schedules, enhancing safety, and improving the reliability of complex systems across diverse sectors, including manufacturing, healthcare, and transportation.