Robust Nonlinear Fault Diagnosis in Input-Output Systems
Robust Nonlinear Fault Diagnosis in Input-Output Systems
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DOI：
10.1080/002071797223659
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发表时间：
1997
影响因子：
2.1
通讯作者：
A. Vemuri;M. Polycarpou
中科院分区：
文献类型：
--
作者：
A. Vemuri;M. Polycarpou
在过去的二十年中，基于模型的解析冗余方法的故障诊断体系结构的设计和分析受到了相当大的关注。在这种故障诊断方案的设计中的关键问题之一是建模的不确定性对它们的性能的影响。针对一类具有模型不确定性的非线性动态系统，当系统的状态并非全部可测时，提出了一种故障诊断算法。这种方法背后的主要思想是监测工厂的任何非标称系统的行为，由于故障利用非线性在线逼近器与可调参数。在线近似器仅使用系统输入和输出测量。一个非线性的估计模型和学习算法的描述，使在线逼近器提供了一个估计的故障。在一定的鲁棒性条件下，证明了非线性故障诊断方案的鲁棒性、灵敏度、稳定性和性能。
The design and analysis of fault diagnosis architectures using the model-based analytical redundancy approach has received considerable attention during the last two decades. One of the key issues in the design of such fault diagnosis schemes is the effect of modelling uncertainties on their performance. This paper describes a fault diagnosis algorithm for a class of nonlinear dynamic systems with modelling uncertainties when not all states of the system are measurable. The main idea behind this approach is to monitor the plant for any off-nominal system behaviour due to faults utilizing a nonlinear online approximator with adjustable parameters. The online approximator only uses the system input and output measurements. A nonlinear estimation model and learning algorithm are described so that the online approximator provides an estimate of the fault. The robustness, sensitivity, stability and performance properties of the nonlinear fault diagnosis scheme are rigorously established under certain assumptio...