Fault Prognosis

Fault prognosis aims to predict the remaining useful life and potential failure modes of complex systems, enabling proactive maintenance and reducing downtime. Current research heavily utilizes machine learning, particularly deep learning architectures like Long Short-Term Memory (LSTM) networks and ensemble methods, often incorporating data from sensors and supervisory control systems. This field is crucial for optimizing maintenance strategies across various industries, from hydropower generation and aerospace to particle accelerators, improving efficiency, safety, and cost-effectiveness. A key challenge remains the development of robust and generalizable models that can handle limited data and diverse failure scenarios, leading to ongoing exploration of transfer learning and hybrid models that combine data-driven and physics-based approaches.