Nonlinear observers with H∞ performance for sensor fault detection and isolation: a linear matrix inequality design procedure

2005 ◽  
Vol 13 (10) ◽  
pp. 1271-1281 ◽  
Author(s):  
Massimiliano Mattei ◽  
Gaetano Paviglianiti ◽  
Valerio Scordamaglia
Keyword(s):  
Fault Detection ◽  
Linear Matrix Inequality ◽  
Design Procedure ◽  
Fault Detection And Isolation ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Sensor Fault ◽  
Nonlinear Observers ◽  
Sensor Fault Detection

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.

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 Integral Sensor Fault Detection and Isolation for Railway Traction Drive

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1543 ◽  
Author(s):  
Fernando Garramiola ◽  
Jon del Olmo ◽  
Javier Poza ◽  
Patxi Madina ◽  
Gaizka Almandoz
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Traction Drive ◽  
Railway Traction ◽  
Sensor Fault Detection

scholarly journals Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

2008 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Fault Detection ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Channel Measurements ◽  
Sensor Fault ◽  
Engine Model ◽  
Dual Channel ◽  
Sensor Fault Detection ◽  
Baseline System ◽  
On Line

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

scholarly journals Neuro-Fuzzy for Sensor Fault Detection and Isolation

2014 ◽  
Vol 90 (17) ◽  
pp. 42-46
Author(s):  
Rajendra Sharma ◽  
Snehal Kokil ◽  
Prtit Khaire
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Neuro Fuzzy ◽  
Sensor Fault Detection
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