Sensor Fault

Sensor fault detection and mitigation is a critical research area aiming to improve the reliability and safety of complex systems, from autonomous vehicles to robotic manipulators and critical infrastructure. Current research focuses on developing robust methods for detecting and isolating faults using diverse approaches, including neural networks (e.g., masked models, transformers, and neural control barrier functions), Bayesian networks, and random forests, often incorporating data-driven techniques and multi-sensor fusion. These advancements are crucial for enhancing the dependability of various technologies and ensuring safe operation in challenging or unpredictable environments.