Linear-Matrix-Inequality-Based Fault Diagnosis for Gas Turbofan Engine Using Eigenstructure Assignment Principle

2013 ◽  
Vol 302 ◽  
pp. 759-764 ◽  
Author(s):  
Yue Liu ◽  
Dao Liang Tan ◽  
Bin Wang ◽  
Xi Wang
Keyword(s):  
Fault Detection ◽  
Linear Matrix Inequality ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Eigenstructure Assignment ◽  
Matrix Inequality ◽  
Turbofan Engine ◽  
Suggested Approach ◽  
Assignment Method

This paper proposes an eigenstructure assignment method for engine control system diagnosis based on disturbance decoupling, since noisy disturbance has an adverse impact on the performance of aircraft engine fault detection and isolation (FDI). In practice, it is often difficult to solve the eigenstructure assignment method, and the result is far from being satisfactory. In view of this, the paper makes an attempt to deal with the issue by linear matrix inequality (LMI). The advantages of the presented method are as follows: first, it can reduce the effect of exogenous disturbance on fault detection; In the meantime, it will not impair sensitivity to system faults. Experimental results show that the suggested approach performs well on the simulation of an advanced turbofan engine.

Robust Unknown Input Observer for Nonlinear Systems and Its Application to Fault Detection and Isolation

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
S. Mondal ◽  
G. Chakraborty ◽  
K. Bhattacharyya
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear System ◽  
Fault Detection ◽  
Nonlinear Function ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Unknown Input ◽  
Matrix Inequality ◽  
Unknown Input Observer ◽  
Linear Matrix Inequality Approach

A robust unknown input observer for a nonlinear system whose nonlinear function satisfies the Lipschitz condition is designed based on linear matrix inequality approach. Both noise and uncertainties are taken into account in deriving the observer. A component fault detection and isolation scheme based on these observers is proposed. The effectiveness of the observer and the fault diagnosis scheme is shown by applying them for component fault diagnosis of an electrohydraulic actuator.

scholarly journals Computation of a Reference Model for Robust Fault Detection and Isolation Residual Generation

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Emmanuel Mazars ◽  
Imad M. Jaimoukha ◽  
Zhenhai Li
Keyword(s):  
Fault Detection ◽  
Reference Model ◽  
Linear Time ◽  
Design Procedure ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Lmi Optimization ◽  
Linear Time Invariant ◽  
Robust Fault Detection

This paper considers matrix inequality procedures to address the robust fault detection and isolation (FDI) problem for linear time-invariant systems subject to disturbances, faults, and polytopic or norm-bounded uncertainties. We propose a design procedure for an FDI filter that aims to minimize a weighted combination of the sensitivity of the residual signal to disturbances and modeling errors, and the deviation of the faults to residual dynamics from a fault to residual reference model, using theℋ∞-norm as a measure. A key step in our procedure is the design of an optimal fault reference model. We show that the optimal design requires the solution of a quadratic matrix inequality (QMI) optimization problem. Since the solution of the optimal problem is intractable, we propose a linearization technique to derive a numerically tractable suboptimal design procedure that requires the solution of a linear matrix inequality (LMI) optimization. A jet engine example is employed to demonstrate the effectiveness of the proposed approach.

scholarly journals LMI-Observer-Based Stabilizer for Chaotic Systems in the Existence of a Nonlinear Function and Perturbation

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1128
Author(s):  
Hamede Karami ◽  
Saleh Mobayen ◽  
Marzieh Lashkari ◽  
Farhad Bayat ◽  
Arthur Chang
Keyword(s):  
Linear Matrix Inequality ◽  
State Feedback ◽  
Chaotic Systems ◽  
Nonlinear Function ◽  
Observer Design ◽  
System Stability ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Estimation Errors ◽  
Suggested Approach

In this study, the observer-based state feedback stabilizer design for a class of chaotic systems in the existence of external perturbations and Lipchitz nonlinearities is presented. This manuscript aims to design a state feedback controller based on a state observer by the linear matrix inequality method. The conditions of linear matrix inequality guarantee the asymptotical stability of the system based on the Lyapunov theorem. The stabilizer and observer parameters are obtained using linear matrix inequalities, which make the state errors converge to the origin. The effects of the nonlinear Lipschitz perturbation and external disturbances on the system stability are then reduced. Moreover, the stabilizer and observer design techniques are investigated for the nonlinear systems with an output nonlinear function. The main advantages of the suggested approach are the convergence of estimation errors to zero, the Lyapunov stability of the closed-loop system and the elimination of the effects of perturbation and nonlinearities. Furthermore, numerical examples are used to illustrate the accuracy and reliability of the proposed approaches.

Iterative linear matrix inequality algorithms for fault detection with unknown inputs

2005 ◽  
Vol 219 (2) ◽  
pp. 161-172 ◽  
Author(s):  
H Wang ◽  
J Lam ◽  
S X Ding ◽  
M Zhong
Keyword(s):  
Fault Detection ◽  
Linear Matrix Inequality ◽  
Linear Time ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Unknown Inputs ◽  
Linear Time Invariant ◽  
Unknown Disturbances ◽  
Linear Time Invariant Systems ◽  
Iterative Linear Matrix Inequality

This paper deals with the fault detection problem for linear time-invariant systems with unknown disturbances. Two separate performance indices are presented to facilitate the design of desirable fault detection observers. Iterative linear matrix inequality (LMI) algorithms are proposed in order to design a fault detection observer that aims at enhancing the fault detection and attenuating the effects due to unknown inputs. Numerical examples are employed to demonstrate the effectiveness of the proposed methods.

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