基于模型参数结构不确定性的故障检测

O. Adrot, D. Maquin, J. Ragot
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引用次数: 42

摘要

本文研究了一种能够考虑有界变量描述的参数不确定性的原始故障检测方法。区间为不确定建模提供了一个有吸引力的替代方案,并允许直接生成自适应阈值。该方法基于奇偶空间方法。本文的出发点是根据状态表示的不确定矩阵来选择宇称矩阵。在表示奇偶关系的外部形式之后,利用区间算法求出内部形式,并推导出划分正常运算域的残差框。然后,我们重点讨论了区间变量之间的依赖所引起的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault detection with model parameter structured uncertainties
This paper focuses on an original fault detection method, able to take into account parameter uncertainties described by bounded variables. Intervals offer an attractive alternative for uncertain modelling and allow a straightforward generation of adaptive thresholds. This method is based on the parity space approach. The original point treated herein concerns the choice of the parity matrix, according to uncertain matrices of state representations. After expressing external forms of parity relations, interval arithmetic is used to evaluate internal forms and to deduce residual enclosures delimiting normal operation field. Then, we stress on the problems caused by dependencies between interval variables.
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