Adaptive locally-linear-models-based fault detection and diagnosis for unmeasured states and unknown faults

F. Soltanian, A. Alvanagh, M. Khosrowjerdi
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引用次数: 1

Abstract

Today the problem of fault detection and diagnosis (FDD) is considered as an important and essential counterpart of control engineering systems. Because of importance and existence of faults that don't have a known structure in control system, i.e., fault occurred because of tangle of complex factors, In this paper a Lipschitz nonlinear system with unmeasured states and unknown faults is considered and a novel FDD architecture for it is presented. A neuro/fuzzy model consisting of few locally linear models (LLMs) with on-line updated centers and width vectors is used to approximate the model of the fault. A nonlinear observer is used to estimate the states of the system that are inputs to LLMs. The stability analysis of system is carried out via Lyapunov theory, from which the parameter updating rules are derived. At the end of this paper some numerical simulation is given to show the effectiveness of the method.
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基于自适应局部线性模型的非测量状态和未知故障检测与诊断
目前,故障检测与诊断(FDD)问题被认为是控制工程系统的一个重要和必不可少的对应问题。针对控制系统中存在结构未知的故障,即复杂因素纠缠引起的故障的重要性和存在性,本文考虑了一种状态不可测且故障未知的Lipschitz非线性系统,提出了一种新的FDD体系结构。采用一种由多个局部线性模型(llm)组成的神经/模糊模型来逼近故障模型,该模型具有在线更新的中心和宽度向量。非线性观测器用于估计作为llm输入的系统状态。利用李雅普诺夫理论对系统进行了稳定性分析,并推导了参数更新规则。最后通过数值仿真验证了该方法的有效性。
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