  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 optimize the gathering of information. The core contribution of
this work is FIERL, a general simulation-based approach for the design of such
control strategies, using Constrained Reinforcement Learning (CRL) to optimize
the performance of arbitrary passive detectors. The control policy is learned
without the need of knowing the passive detector inner workings, making FIERL
broadly applicable. However, it is especially useful when paired with the
design of an efficient passive component. Unlike most AFD approaches, FIERL can
handle fairly complex scenarios such as continuous sets of fault modes. The
effectiveness of FIERL is tested on a benchmark problem for actuator fault
diagnosis, where FIERL is shown to be fairly robust, being able to generalize
to fault dynamics not seen in training.
