Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction and the measurements are zero-mean, white, Gaussian random variables. In a turbine engine diagnosis, however, this assumption does not always hold due to the presence of biases in the model. This is especially true for a transient operation. As a result, the estimated parameters tend to diverge from their actual values, which strongly degrades the diagnosis. The purpose of this contribution is to present a Kalman filter diagnosis tool where the model biases are treated as an additional random measurement error. The new methodology is tested on simulated transient data representative of a current turbofan engine configuration. While relatively simple to implement, the newly developed diagnosis tool exhibits a much better accuracy than the original Kalman filter in the presence of model biases.
Skip Nav Destination
e-mail: s.borguet@ulg.ac.be
e-mail: p.dewallef@gmail.com
e-mail: o.leonard@ulg.ac.be
Article navigation
Research Papers
A Way to Deal With Model-Plant Mismatch for a Reliable Diagnosis in Transient Operation
S. Borguet,
S. Borguet
Turbomachinery Group,
e-mail: s.borguet@ulg.ac.be
University of Liège
, Chemin des Chevreuils 1, 4000 Liège, Belgium
Search for other works by this author on:
P. Dewallef,
P. Dewallef
Turbomachinery Group,
e-mail: p.dewallef@gmail.com
University of Liège
, Chemin des Chevreuils 1, 4000 Liège, Belgium
Search for other works by this author on:
O. Léonard
O. Léonard
Turbomachinery Group,
e-mail: o.leonard@ulg.ac.be
University of Liège
, Chemin des Chevreuils 1, 4000 Liège, Belgium
Search for other works by this author on:
S. Borguet
Turbomachinery Group,
University of Liège
, Chemin des Chevreuils 1, 4000 Liège, Belgiume-mail: s.borguet@ulg.ac.be
P. Dewallef
Turbomachinery Group,
University of Liège
, Chemin des Chevreuils 1, 4000 Liège, Belgiume-mail: p.dewallef@gmail.com
O. Léonard
Turbomachinery Group,
University of Liège
, Chemin des Chevreuils 1, 4000 Liège, Belgiume-mail: o.leonard@ulg.ac.be
J. Eng. Gas Turbines Power. May 2008, 130(3): 031601 (8 pages)
Published Online: March 26, 2008
Article history
Received:
June 20, 2006
Revised:
October 29, 2007
Published:
March 26, 2008
Citation
Borguet, S., Dewallef, P., and Léonard, O. (March 26, 2008). "A Way to Deal With Model-Plant Mismatch for a Reliable Diagnosis in Transient Operation." ASME. J. Eng. Gas Turbines Power. May 2008; 130(3): 031601. https://doi.org/10.1115/1.2833491
Download citation file:
Get Email Alerts
Cited By
Experimental Identification Of Blade Tip Rub Forces At Engine Relevant Temperatures And Speeds
J. Eng. Gas Turbines Power
Study Of Tandem Rotor Dual Wake Interaction With Downstream Stator Under Unsteady Numerical Approach
J. Eng. Gas Turbines Power
Experimental Design Validation of a Swirl-Stabilized Burner With Fluidically Variable Swirl Number
J. Eng. Gas Turbines Power (April 2025)
Experimental Characterization of a Bladeless Air Compressor
J. Eng. Gas Turbines Power (April 2025)
Related Articles
An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation
J. Eng. Gas Turbines Power (January,2008)
Observer-Based Cylinder Air Charge Estimation for Spark-Ignition Engines
J. Eng. Gas Turbines Power (October,2017)
A Generalized Likelihood Ratio Test for Adaptive Gas Turbine Performance Monitoring
J. Eng. Gas Turbines Power (January,2009)
Estimation of Intake Oxygen Concentration Using a Dynamic Correction State With Extended Kalman Filter for Light-Duty Diesel Engines
J. Dyn. Sys., Meas., Control (January,2018)
Related Proceedings Papers
Related Chapters
Machine Learning Methods for Data Assimilation
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
Position Controller for DC Motor Using PI Controller with KALMAN Filter
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)
Real-Time Prediction Using Kernel Methods and Data Assimilation
Intelligent Engineering Systems through Artificial Neural Networks