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Data‐driven fault detection for linear systems: A q‐step residual iteration approach
International Journal of Robust and Nonlinear Control ( IF 3.2 ) Pub Date : 2020-07-15 , DOI: 10.1002/rnc.5017
Xiao‐Lei Wang 1, 2 , Guang‐Hong Yang 1, 3 , Dianhua Zhang 2
Affiliation  

This article studies the problem of data‐driven (DD) fault detection (FD) for linear systems. First, based on the DD realization of kernel representation, new residual generators are designed via a q‐step residual iteration method. Then, it is proved that the proposed residual generators guarantee the stability and L1 performance of the FD error systems, and the presented residual design method is more sensitive to faults than the existing ones constructed directly from available process data. Finally, two simulation examples are provided to verify the effectiveness and advantages of the designed method.

中文翻译:

线性系统的数据驱动故障检测:q步残差迭代方法

本文研究线性系统的数据驱动(DD)故障检测(FD)问题。首先,基于核表示的DD实现,通过aq步残差迭代方法设计了新的残差生成器。然后,证明了所提出的残差生成器保证了FD误差系统的稳定性和L 1性能,并且与直接从可用过程数据中构造的现有残差设计方法相比,所提出的残差设计方法对故障更敏感。最后,提供了两个仿真示例,以验证所设计方法的有效性和优势。
更新日期:2020-07-15
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