Adaptive Observer Design for Sensor Fault Detection and Reconstruction

2019 ◽  
Vol 20 (9) ◽  
pp. 515-523
Author(s):  
N. Bedioui ◽  
R. Houimli ◽  
M. Besbes
Keyword(s):  
Fault Detection ◽  
Sufficient Conditions ◽  
Main Idea ◽  
Observer Design ◽  
Adaptive Observer ◽  
Linear Matrix ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Robust Adaptive ◽  
The Stability

A new approach is presented for sensor fault detection reconstruction and state estimation. The system considered is linear polytopic parameter-vary ing (LPV) system. The main idea is the design of a novel robust adaptive observer based on and polyquadratic formulation with a new set of relaxation. Sufficient conditions are given by a set of Linear Matrix Inequalities (LMI) in order to guarantee the stability of the system and the asymptotic convergence of the fault error. A simulation example has been studied to illustrate the proposed methods by detecting constant and variable sensor fault.

scholarly journals Robust sensor fault estimation for descriptor-LPV systems with unmeasurable gain scheduling functions: Application to an anaerobic bioreactor

2015 ◽  
Vol 25 (2) ◽  
pp. 233-244 ◽  
Author(s):  
Francisco-Ronay López-Estrada ◽  
Jean-Christophe Ponsart ◽  
Carlos-Manuel Astorga-Zaragoza ◽  
Jorge-Luis Camas-Anzueto ◽  
Didier Theilliol
Keyword(s):  
Sufficient Conditions ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Anaerobic Bioreactor ◽  
Lpv Systems ◽  
Sensor Fault Detection ◽  
System States ◽  
Sensor Fault Estimation

Abstract This paper addresses the design of a state estimation and sensor fault detection, isolation and fault estimation observer for descriptor-linear parameter varying (D-LPV) systems. In contrast to where the scheduling functions depend on some measurable time varying state, the proposed method considers the scheduling function depending on an unmeasurable state vector. In order to isolate, detect and estimate sensor faults, an augmented system is constructed by considering faults to be auxiliary state vectors. An unknown input LPV observer is designed to estimate simultaneously system states and faults. Sufficient conditions to guarantee stability and robustness against the uncertainty provided by the unmeasurable scheduling functions and the influence of disturbances are synthesized via a linear matrix inequality (LMI) formulation by considering H∞ and Lyapunov approaches. The performances of the proposed method are illustrated through the application to an anaerobic bioreactor model.

Sensor Fault Detection, Diagnosis and Validation - A Survey

2012 ◽  
Vol 229-231 ◽  
pp. 1265-1271 ◽  
Author(s):  
Zhi Gang Yao ◽  
Li Cheng ◽  
Qing Lin Wang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Principal Components ◽  
Sufficient Conditions ◽  
Data Driven ◽  
Sensor Fault ◽  
Validity Index ◽  
Sensor Fault Detection ◽  
Multiple Sensor ◽  
Necessary And Sufficient

This paper provides an overview and analysis of data-driven sensor fault detection, diagnosis and validation from the application viewpoint. The typical sensor fault detection indices in the literature and the fundamental issues of necessary and sufficient conditions for detectability, reconstructability, identifiability and isolatability are analyzed. The main objective is to study the essential and important algorithms and techniques for single or multiple sensor fault diagnosis and validation. The issues of optimal principal components, sensor validity index, maximized sensitivity, as well as robust sensor fault diagnosis, etc. are discussed. Additional focuses are summarized at the end of the paper for future investigation.

scholarly journals Fault Detection for Wireless Network Control Systems with Stochastic Uncertainties and Time Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Zhang ◽  
Pengfei Guo ◽  
Ming Lyu ◽  
Hamid Reza Karimi ◽  
Yuming Bo
Keyword(s):  
Fault Detection ◽  
Control Systems ◽  
Wireless Network ◽  
Time Delays ◽  
Sufficient Conditions ◽  
Network Control ◽  
Linear Matrix ◽  
Network Control Systems ◽  
The Stability ◽  
Stochastic Uncertainties

The fault detection problem is investigated for a class of wireless network control systems which has stochastic uncertainties in the state-space matrices, combined with time delays and nonlinear disturbance. First, the system error observer is proposed. Then, by constructing proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the fault detection observer for the discrete system, and observer gain is also derived by solving linear matrix inequalities. Finally, a simulation example shows that when a fault happens, the observer residual rises rapidly and fault can be quickly detected, which demonstrates the effectiveness of the proposed method.

scholarly journals H_/H∞ fault detection observer design for a polytopic LPV system using the relative degree

2018 ◽  
Vol 28 (1) ◽  
pp. 83-95 ◽  
Author(s):  
Meng Zhou ◽  
Mickael Rodrigues ◽  
Yi Shen ◽  
Didier Theilliol
Keyword(s):  
Fault Detection ◽  
Optimization Problem ◽  
Time Derivative ◽  
Observer Design ◽  
Relative Degree ◽  
Linear Matrix ◽  
Sensor Fault ◽  
Linear Parameter Varying ◽  
Output Vector ◽  
Fault Sensitivity

Abstract This paper proposes an H_/H∞ fault detection observer method by using generalized output for a class of polytopic linear parameter-varying (LPV) systems. As the main contribution, with the aid of the relative degree of output, a new output vector is generated by gathering the original output and its time derivative, and it is feasible to consider H_ actuator fault sensitivity in the entire frequency for the new system. In order to improve actuator and sensor fault sensitivity as well as guarantee robustness against disturbances, simultaneously, an H_/H∞ fault detection observer is designed for the new LPV polytopic system. Besides, the design conditions of the proposed observer are transformed into an optimization problem by solving a set of linear matrix inequalities (LMIs). Numerical simulations are provided to illustrate the effectiveness of the proposed method.

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