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.

这篇论文提出了一种新颖的主动故障检测（AFD）方法，通过明确将任务分为两个部分：被动故障检测（PFD）和控制输入设计。该方法具有很强的普适性，大部分已有的 AFD 文献都可以从该角度来解读。通过认识到这种分离，PFD 方法可以提供高效利用可用信息的组件，而控制输入的设计则是为了优化信息收集。本文的核心贡献是 FIERL，这是一种通用的基于模拟的方法，用于设计这种控制策略，使用约束强化学习（CRL）来优化任意被动探测器的性能。控制策略是在不需要了解被动探测器内部工作原理的情况下学习的，使得 FIERL 具有广泛的适用性。然而，当与高效被动组件的设计相结合时，其特别有用。与大多数 AFD 方法不同，FIERL 可以处理连续的故障模式等相当复杂的场景。FIERL 的有效性在执行器故障诊断的基准问题上进行了测试，结果显示其具有相当的鲁棒性，能够泛化到训练中未见过的故障动态。