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

• Published in: International journal of advanced manufacturing technology Vol. 90; no. 5-8; pp. 1317 - 1336
• Main Authors: Najeh, Tawfik, Ben Njima, Chakib, Garna, Tarek, Ragot, José
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2017; Springer Nature B.V; Springer Verlag
• Subjects: 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
• DOI: 10.1007/s00170-016-9414-6

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.