A Fault Detection Approach for Nonlinear Systems Based on Data-Driven Realizations of Fuzzy Kernel Representations
A Fault Detection Approach for Nonlinear Systems Based on Data-Driven Realizations of Fuzzy Kernel Representations
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DOI：
10.1109/tfuzz.2017.2752136
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发表时间：
2018-08
影响因子：
11.9
通讯作者：
Linlin Li;S. Ding;Ying Yang;Kai-xiang Peng;Jianbin Qiu
中科院分区：
文献类型：
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作者：
Linlin Li;S. Ding;Ying Yang;Kai-xiang Peng;Jianbin Qiu
本文主要研究非线性系统的数据驱动故障检测问题。为了达到我们的目的，首先介绍了非线性系统核表示的Takagi-Sugeno模糊数据驱动形式的定义，这是我们工作的基础。主要贡献包括两个部分。在第一部分中，提出了一种数据驱动的模糊过程建模方法，并与之相关联，一些建模问题得到解决的援助，所谓的随机算法技术的概率框架。其次是数据驱动实现的模糊核表示及其在故障检测系统设计中的实现。为了将数据驱动方法与基于模糊核的故障检测方法相结合，提出了模糊核表示的递归形式。第二部分基于递归模糊核表示，研究了基于模糊观测器的故障检测设计方案。我们的研究的主要结果说明了一个实验室设置的三槽系统的实验研究。
This paper is devoted to the data-driven fault detection of nonlinear systems. For our purpose, the definition of Takagi–Sugeno fuzzy data-driven forms of kernel representations for nonlinear systems is introduced first, which builds the basis of our work. The major contributions consist of two parts. In the first part, a data-driven method for fuzzy process modeling is proposed, and associated with it, some modeling issues are addressed with the aid of the so-called randomized algorithm technique in the probabilistic framework. It is followed by a data-driven realization of fuzzy kernel representation and its implementation in the fault detection system design. To link the data-driven methods to the well-established observer-based fault detection approaches, the recursive form of the fuzzy kernel representation is proposed. In the second part, the fuzzy-observer-based fault detection design scheme is investigated based on the recursive fuzzy kernel representation. The main results of our study are illustrated by an experimental study on the laboratory setup of a three-tank system.