This paper introduces a Fault Diagnosis (Detection, Isolation, and Estimation) method using Set-Membership Estimation (SME) designed for a class of nonlinear systems that are linear to the fault parameters. The methodology advances fault diagnosis by continuously evaluating an estimate of the fault parameter and a feasible parameter set where the true fault parameter belongs. Unlike previous SME approaches, in this work, we address nonlinear systems subjected to both input and output uncertainties by utilizing inclusion functions and interval arithmetic. Additionally, we present an approach to outer-approximate the polytopic description of the feasible parameter set by effectively balancing approximation accuracy with computational efficiency resulting in improved fault detectability. Lastly, we introduce adaptive regularization of the parameter estimates to enhance the estimation process when the input-output data are sparse or non-informative, enhancing fault identifiability. We demonstrate the effectiveness of this method in simulations involving an Autonomous Surface Vehicle in both a path-following and a realistic collision avoidance scenario, underscoring its potential to enhance safety and reliability in critical applications.

翻译：本文针对一类对故障参数呈线性关系的非线性系统，提出了一种采用集合成员估计的故障诊断（检测、隔离与估计）方法。该方法通过持续评估故障参数的估计值以及真实故障参数所属的可行参数集，推进了故障诊断的发展。与以往的集合成员估计方法不同，本工作利用包含函数和区间运算，处理同时受输入和输出不确定性的非线性系统。此外，我们提出了一种有效平衡近似精度与计算效率的方法，以外部近似可行参数集的多面体描述，从而提高了故障可检测性。最后，我们引入了参数估计的自适应正则化，以在输入-输出数据稀疏或信息量不足时增强估计过程，从而提高故障可辨识性。我们在涉及自主水面航行器的路径跟随和现实避碰场景的仿真中验证了该方法的有效性，突显了其在关键应用中提升安全性与可靠性的潜力。