Post Fault Trajectory

Post-fault trajectory analysis focuses on identifying and understanding system behavior after a fault occurs, aiming to improve fault diagnosis, mitigation, and system resilience. Current research emphasizes automated fault detection and localization using machine learning models, including convolutional neural networks, random forests, and graph convolutional networks, often applied to diverse data sources like logs, sensor readings, and images. This work is significant for improving the reliability and safety of various systems, from power grids and manufacturing processes to aerospace and robotics, by enabling faster fault identification and more effective preventative maintenance strategies.