On Inverse Problems in Turbine Engine Parameter Estimation

Abstract

This paper extends previous work on model order reduction based on singular value decomposition. It is shown how the decrease in estimator variance must be balanced against the bias on the estimate inevitably introduced by solving the inverse problem in a reduced order space. A proof for the decrease in estimator variance by means of multi-point analysis is provided. The proof relies on comparing the Cramer-Rao lower bound of the single point and the multi-point estimators. Model order selection is discussed in the presence of a varying degree of a priori parameter information, through the use of a regularization parameter. Simulation results on the SR-30 turbojet engine indicate that the theoretically attainable multi-point improvements are difficult to realize in practical jet engine applications.

Evaluation of gas path analysis methods for gas turbine diagnosis

“…Although all the works implementing the multipoint approach agree that the idea more or less improves the diagnostic effectiveness, there are also results (see Henriksson et al [18]), indicating that the theoretically attainable multi-point improvements are difficult to realize in practical engine applications. In order to understand the reasons for potential problems concerning diagnosis using a multipoint approach, it is necessary to examine the underlying assumptions of the method.…”

Engine Condition Monitoring and Diagnostics

“…Additional details on assessing identifiability in multipoint gas turbine estimation problems are given in [15]. Although all the works implementing the multipoint approach agree that the idea more or less improves the diagnostic effectiveness, there are also results (see [16]), indicating that the theoretically attainable multi-point improvements are difficult to realize in practical engine applications.…”