Fault Identification Enhancement with Reinforcement Learning (FIERL)

arXiv:2405.04938v1 Announce Type: new
Abstract: This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control input design. This formulation is very general, and most existing AFD literature can be viewed through this lens. By recognizing this separation, PFD methods can be leveraged to provide components that make efficient use of the available information, while the control input is designed in order to …

abstract arxiv control cs.lg design detection general identification lens literature novel reinforcement reinforcement learning through type