Research in Engineering and Aviation
Nonlinear weighted-least-squares estimation approach for gas-turbine diagnostic applications
Author(s): Li, Y.G., & Korakianitis, T.
Journal: Journal of Propulsion and Power, 27(2), pp. 337-345. DOI: 10.2514/1.47129
Gas-turbine engines are subject to degradation, or even failure, during their operation. Effective diagnosis of engine health would provide maintenance engineers with important engine health information, which can be used to achieve high engine availability and low maintenance costs. In this paper a novel gas-turbine health estimation technique is introduced, which is called nonlinear weighted-least-squares estimation. The nonlinear weighted-leastsquares diagnostic method is an improvement to the conventional linear weighted-least-squares diagnostic method. It aims to provide an effective alternative to gas-turbine gas-path diagnostic analysis for condition monitoring. In the nonlinear weighted-least-squares estimation method the gas-turbine health parameters are estimated by minimizing the summation of weighted square deviations between estimated and actual values of gas-turbine performance measurements. The measurement uncertainties associated with gas-path measurements are taken into account by using a weighting matrix. The concepts of fault cases and gas-path-analysis index are introduced. These provide a new diagnostic approach, enabling fault isolation and enhancing the confidence of diagnostic results. This diagnostic approach allows the typically nonlinear gas-turbine thermodynamic-performance models to be directly used in condition monitoring while taking into account performance nonlinearities. An iterative calculation process is introduced to obtain a converged estimation of engine degradation. The nonlinear weighted-least-squares diagnostic approach has been applied to a model industrial gas-turbine engine to test its effectiveness. The numerical tests of the new diagnostic approach show that with appropriate selection of engine gas-path measurements, the method can be used effectively and successfully to predict gas-turbine performance degradation.