针对非线性故障系统，用区间方法实现高可靠故障诊断。
Set-Membership Estimation for Fault Diagnosis of Nonlinear Systems
- 基于区间算术和包含函数处理输入输出不确定性
- 外逼近可行参数集，平衡精度与计算效率，提升可检测性
- 自适应正则化应对数据稀疏，适合复杂工程场景
本文提出一种基于集合成员估计（SME）的故障诊断方法，适用于对故障参数呈线性的非线性系统。该方法通过持续评估故障参数的估计值及其真值所属的可行参数集，实现故障检测、隔离与估计。相较于以往SME方法，本工作采用包含函数与区间算术，有效处理同时存在的输入与输出不确定性。此外，提出一种外逼近多面体可行参数集的方法，在保证近似精度的同时兼顾计算效率，从而提升故障可检测性。最后，引入自适应正则化机制，增强在输入输出数据稀疏或非信息性条件下的参数估计能力，改善故障可辨识性。通过自主水面车辆在路径跟踪与真实碰撞避让场景中的仿真验证，表明该方法在提升关键应用安全性和可靠性方面具有潜力。
原文摘要 · Abstract (English)
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.
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