Input fault detection and estimation using PI observer based on the ARX-Laguerre model

This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from...

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Vydáno v:	International journal of advanced manufacturing technology Ročník 90; číslo 5-8; s. 1317 - 1336
Hlavní autoři:	Najeh, Tawfik, Ben Njima, Chakib, Garna, Tarek, Ragot, José
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.05.2017 Springer Nature B.V Springer Verlag
Témata:	Actuators Automatic CAE) and Design Computer simulation Computer-Aided Engineering (CAD Engineering Engineering Sciences Fault detection Genetic algorithms Industrial and Production Engineering Linear matrix inequalities Mathematical analysis Mathematical models Matrix methods Mechanical Engineering Media Management Original Article Proportional integral Synthesis ARX-Laguerre model LMI Pole optimization Diagnosis Genetic algorithm PIO
ISSN:	0268-3768, 1433-3015
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Abstract	This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2 n d order electrical linear system.
AbstractList	This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2nd order electrical linear system. This work is dedicated to the synthesis of a new fault detection and identification scheme for the actuator and/or sensor faults modeled as unknown inputs of the system. The novelty of this scheme consists in the synthesis of a new structure of proportional-integral observer (PIO) reformulated from the new linear ARX-Laguerre representation with filters on system input and output in order to estimate the unknown inputs presented as faults. The designed observer exploits the input/output measurements to reconstruct the Laguerre filter outputs where the stability and the convergence properties are ensured by using Linear Matrix Inequality. However, a significant reduction of this model is subject to an optimal choice of both Laguerre poles which is achieved by a new proposed identification approach based on a genetic algorithm. The performances of the proposed identification approach and the resulting PIO are tested on numerical simulation and validated on a 2 n d order electrical linear system.
Author	Najeh, Tawfik Garna, Tarek Ben Njima, Chakib Ragot, José
Author_xml	– sequence: 1 givenname: Tawfik surname: Najeh fullname: Najeh, Tawfik email: najehtawfik@gmail.com organization: Laboratory of Automatic Control, Signal and Image Processing, National Engineering School of Monastir – sequence: 2 givenname: Chakib surname: Ben Njima fullname: Ben Njima, Chakib organization: Laboratory of Automatic Control, Signal and Image Processing, National Engineering School of Monastir – sequence: 3 givenname: Tarek surname: Garna fullname: Garna, Tarek organization: Laboratory of Automatic Control, Signal and Image Processing, National Engineering School of Monastir – sequence: 4 givenname: José surname: Ragot fullname: Ragot, José organization: Center of Research in Automatic of Nancy, University of Lorraine and CNRS (National Centre for Scientific Research)
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Cites_doi	10.1109/TAC.2002.805675 10.1080/00207178908953350 10.1109/78.827551 10.1007/978-3-7091-6711-3 10.1002/acs.1260 10.1109/9.76361 10.1109/78.414769 10.1002/acs.2522 10.1080/00207170010025249 10.1080/00207179.2012.732710 10.1109/9.222305 10.1007/s00170-015-7099-x 10.1016/j.isatra.2012.06.005 10.1016/j.mcm.2003.06.009 10.1016/0098-3004(94)90072-8 10.1080/00207729508929120
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Keywords	ARX-Laguerre model LMI Pole optimization Diagnosis Genetic algorithm PIO
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SubjectTerms	Actuators Automatic CAE) and Design Computer simulation Computer-Aided Engineering (CAD Engineering Engineering Sciences Fault detection Genetic algorithms Industrial and Production Engineering Linear matrix inequalities Mathematical analysis Mathematical models Matrix methods Mechanical Engineering Media Management Original Article Proportional integral Synthesis
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Title	Input fault detection and estimation using PI observer based on the ARX-Laguerre model
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