A sensor-fault-tolerant diagnosis tool based on a quadratic programming approach

[en] Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm gives a good estimate of the engine condition provided that the discrepancies between the model prediction and the measurements are Zero-mean, white random variables. However this assumption is not verified when instrumentation (sensor) faults occur As a result, the identified health parameters tend to diverge from their actual values, which strongly deteriorates the diagnosis. The purpose of this contribution is to blend robustness against sensor faults into a tool for performance monitoring of jet engines. To this end, a robust estimation approach is considered and a sensor-fault detection and isolation module is derived. It relies on a quadratic program to estimate the sensor faults and is integrated easily with the original diagnosis tool. The improvements brought by this robust estimation approach are highlighted through a series of typical test cases that may be encountered on current turbine engines.

Disciplines :

Materials science & engineering
Space science, astronomy & astrophysics
Aerospace & aeronautics engineering
Mechanical engineering

Author, co-author :

Borguet, Sébastien ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale

Léonard, Olivier ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Turbomachines et propulsion aérospatiale

Language :

English

Title :

A sensor-fault-tolerant diagnosis tool based on a quadratic programming approach

Publication date :

2008

Journal title :

Journal of Engineering for Gas Turbines and Power

ISSN :

0742-4795

eISSN :

1528-8919

Publisher :

American Society of Mechanical Engineers, New York, United States - New York

Volume :

130

Issue :

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 26 August 2009

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Bibliography

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