Open Access
A simulation study of turbofan engine deterioration estimation using Kalman filtering techniques
TLDR
In this article, a Kalman filter design is presented for estimating two performance parameters that account for engine deterioration: high and low pressure turbine delta efficiencies, which can be estimated with an accuracy of + or - 0.25 percent.Abstract:
Deterioration of engine components may cause off-normal engine operation. The result is an unecessary loss of performance, because the fixed schedules are designed to accommodate a wide range of engine health. These fixed control schedules may not be optimal for a deteriorated engine. This problem may be solved by including a measure of deterioration in determining the control variables. These engine deterioration parameters usually cannot be measured directly but can be estimated. A Kalman filter design is presented for estimating two performance parameters that account for engine deterioration: high and low pressure turbine delta efficiencies. The delta efficiency parameters model variations of the high and low pressure turbine efficiencies from nominal values. The filter has a design condition of Mach 0.90, 30,000 ft altitude, and 47 deg power level angle (PLA). It was evaluated using a nonlinear simulation of the F100 engine model derivative (EMD) engine, at the design Mach number and altitude over a PLA range of 43 to 55 deg. It was found that known high pressure turbine delta efficiencies of -2.5 percent and low pressure turbine delta efficiencies of -1.0 percent can be estimated with an accuracy of + or - 0.25 percent efficiency with a Kalman filter. If both the high and low pressure turbine are deteriorated, the delta efficiencies of -2.5 percent to both turbines can be estimated with the same accuracy.read more
Citations
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Journal ArticleDOI
Kalman filtering with state equality constraints
Dan Simon,Tien Li Chia +1 more
TL;DR: In this article, a rigorous analytic method of incorporating state equality constraints in the Kalman filter is developed, which significantly improves the prediction accuracy of the filter and is demonstrated on a simple nonlinear vehicle tracking problem.
Proceedings ArticleDOI
Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics
TL;DR: In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults.
Journal ArticleDOI
Kalman filtering with inequality constraints for turbofan engine health estimation
Dan Simon,Donald L. Simon +1 more
TL;DR: In this article, two analytical methods to incorporate state-variable inequality constraints into the Kalman filter are derived, one is a general technique that uses hard constraints to enforce inequalities on the state variable estimates and the other is a soft constraint that is required to be approximately satisfied rather than exactly satisfied.
Journal ArticleDOI
Constrained Kalman filtering via density function truncation for turbofan engine health estimation
Dan Simon,Donald L. Simon +1 more
TL;DR: It is shown that the truncated Kalman filter may provide a more accurate way of incorporating inequality constraints than other constrained filters (e.g. the projection approach to constrained filtering).
Journal ArticleDOI
Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering
Dan Simon,Donald L. Simon +1 more
TL;DR: In this article, an analytic method of in-corporating state variable inequality constraints in the Kalman¯lter is developed, which is used to estimate the state variables of a dynamic system.
References
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Book
Linear Optimal Control Systems
Huibert Kwakernaak,Raphael Sivan +1 more
TL;DR: In this article, the authors provide an excellent introduction to feedback control system design, including a theoretical approach that captures the essential issues and can be applied to a wide range of practical problems.
Journal ArticleDOI
Treatment of bias in recursive filtering
TL;DR: In this article, the problem of estimating the state x of a linear process in the presence of a constant but unknown bias vector b is considered, and it is shown that the optimum estimate \hat{x} of the state can be expressed as x + V_{x}\hat{b} (1) where x is the bias-free estimate, computed as if no bias were present, and V x is a matrix which can be interpreted as the ratio of the covariance of \tilde{x] and b to the variance of b.
Book
Lectures on Wiener and Kalman Filtering
TL;DR: In this paper, the authors consider two random variables X, Y with a known joint density function fx,y(.,.). Assume that in a particular experiment, the random variable Y can be measured and takes the value y. What can be said about the corresponding value of the unobservable variable X?