Publication: Robust observer-based residual generation for fault detection and estimation in linear uncertain discrete-time system
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Date
2022-08-01
Authors
Ahmad, Masood
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Abstract
With the ongoing increase in system complexity, less tolerance to performance degradation and safety requirements of many industrial systems have increased the demand of Fault Detection (FD) because fault leads to reduced efficiency or even complete failure of the entire system. In this thesis, fault detection and estimation problem for the class of Linear Discrete-Time Invariant (LDTI) systems subject to model uncertainties and unknown inputs are addressed using observer-based technique. Two types of model uncertainties (i.e., norm bounded uncertainty and stochastic uncertainty) are considered in the linear dynamical system states, input, and output matrices simultaneously. 𝐻𝐻∞ disturbance attenuation filter, adaptive threshold, model matching technique-based Fault Detection Filter (FDF) and Proportional Integral (PI) observer are designed to detect and estimate the fault
in the monitored system. The core of all the proposed techniques is 𝐻𝐻∞ norm minimization using Bounded Real Lemma (BRL) in Linear Matrix Inequality (LMI) framework. From 𝐻𝐻∞ minimization, residual is further processed using signal norms and a suitable threshold is designed for successful fault detection. Several kinds of faults are tested in the Direct Current (DC) motor and three tank system. From results, all the proposed methods are capable to generate a robust residual that is insensitive to disturbance and model uncertainties, but sensitive to fault. All the tested faults are successfully detected and estimated that confirm the effectiveness of the proposed methods.