초록 ▼

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs such as thrust. The engine's performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters

A new linear point design technique is presented for the determination of tuning parameters that enable the optimal estimation of unmeasured engine outputs such as thrust. The engine's performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters related to each major engine component. Accurate thrust reconstruction depends upon knowledge of these health parameters, but there are usually too few sensors to be able to estimate their values. In this new technique, a set of tuning parameters is determined which accounts for degradation by representing the overall effect of the larger set of health parameters as closely as possible in a least squares sense. The technique takes advantage of the properties of the singular value decomposition of a matrix to generate a tuning parameter vector of low enough dimension that it can be estimated by a Kalman filter. A concise design procedure to generate a tuning vector that specifically takes into account the variables of interest is presented. An example demonstrates the tuning parameters' ability to facilitate matching of both measured and unmeasured engine outputs, as well as state variables. Additional properties of the formulation are shown to lend themselves well to diagnostics.

대표청구항 ▼

I claim: 1. A method of controlling an in-flight engine by using health parameters, said method comprising measuring less than all engine health parameters and using a tuning factor q to input information concerning the unmeasured engine health parameters p, employing a computer to determine the re

I claim: 1. A method of controlling an in-flight engine by using health parameters, said method comprising measuring less than all engine health parameters and using a tuning factor q to input information concerning the unmeasured engine health parameters p, employing a computer to determine the relationship of a tuning factor q to the unmeasured health parameters p; and estimating performance of said engine. 2. The method according to claim 1 wherein an unmeasured parameter p is thrust. 3. The method of claim 1, wherein the relationship between q and the health parameters satisfies the expression: [ x . q . ] = [ A U L * 0 0 ] ⁡ [ x q ] + [ B 0 ] ⁢ u + e ⁢ ⁢ y = [ C U M * ] ⁡ [ x q ] + Du + w ⁢ ⁢ z = [ E U N * ] ⁡ [ x q ] + Fu where x is the vector of state variables, y is the vector of measured outputs, u is the vector of control inputs, z is the vector of auxiliary (unmeasured) model-based outputs, p is a vector representing unmeasured engine health parameters and q is a tuning vector, and where in order for tuning vector q to obtain as much of the information contained in p as possible, p is mapped into q through an optimal transformation matix V* such that q=V*p, and where U* is an optimal transformation matrix, and A, B, C, D, E, F, L, M, and N are system matrices. 4. The method of claim 1 wherein the engine is a turbofan engine. 5. The method of claim 1, wherein the determination detects a fault in an unmeasured parameter. 6. The method of claim 5, wherein the fault determined is at least one from the group consisting of fan efficiency, fan flow capacity, low pressure compressor efficiency, low pressure compressor flow capacity; high pressure compressor efficiency; high pressure compressor flow capacity; high pressure turbine efficiency; high pressure turbine flow capacity; low pressure turbine efficiency and low pressure turbine flow capacity. 7. The method of claim 5, where the unmeasured parameter is one selected from the group consisting of gross thrust and net thrust. 8. The method of claim 5, wherein the fault is isolated. 9. The method of claim 1 wherein engine component performance is estimated by health parameters represented in the following state-space equations: {dot over (x)}=Ax+Lh+Bucmd y=Cx+Mh+Ducmd+v where the vector x represents the state variables, h represents health parameters, ucmd represents control variables, y represents the sensor measurement vector which is corrupted by the noise vector v; and A, B, C, D, L and M are system matrices; and wherein the health parameters are not measured and are treated as part of the augmented state to be estimated. 10. The method of claim 9 wherein variance of health parameters h induce shifts in other variables as health parameters h move away from their nominal values. 11. The method of claim 9 wherein p is a vector representing unmeasured health parameters and the p vector is mapped into the tuning factor q through a transformation matrix V* such that q=V*p. 12. The method of claim 1 wherein the engine being controlled is rated by health parameters, the engine having a number of sensors that measures a subset of health parameters, and wherein the method of determining tuning factor q that enable the estimation of unmeasured health parameters p comprises using a modified Kalman filter to estimate the health parameters by estimating health parameter deviations to control an inflight engine by employing a computer to determine the relationship of the tuning factor q to unmeasured parameters, and wherein unmeasured auxiliary parameters of the engine that are affected by unmeasured health parameters p are estimated in order to determine whether or not the engine is operating properly. 13. The method of claim 12 wherein the number of elements in the tuning vector q is less than the total number of health parameters.