Prognostic and Health Management

Prognostics and Health Management (PHM) focuses on predicting and managing the health of complex systems, aiming to prevent failures and optimize maintenance. Current research emphasizes leveraging large-scale foundation models, like those based on transformer architectures, to improve the generalization, interpretability, and efficiency of PHM systems, particularly within industrial cyber-physical systems. These advanced models, combined with signal processing techniques and deep learning algorithms such as convolutional and recurrent neural networks, are applied to diverse data sources to enhance fault detection, diagnosis, and remaining useful life prediction across various industries. The resulting improvements in reliability, safety, and cost-effectiveness have significant implications for manufacturing, aerospace, and other sectors.