SCIENTIFIC AND TECHNICAL AEROSPACE REPORTS

A Biweekly Publication of the National Aeronautics and Space Administration
VOLUME 44, ISSUE 10 - MAY 19, 2006

07 AIRCRAFT PROPULSION AND POWER
Includes primary propulsion systems and related systems and components, e.g., gas turbine engines, compressors, and fuel systems; and onboard auxiliary power plants for aircraft.

For related information see also 20 Spacecraft Propulsion and Power; 28 Propellants and Fuels; and 44 Energy Production and Conversion.

20060012173 NASA Glenn Research Center, Cleveland, OH, USA, Cleveland State Univ., Cleveland, OH, USA

Constrained Kalman Filtering Via Density Function Truncation for Turbofan Engine Health Estimation

Simon, Dan; Simon, Donald L.; April 2006; 27 pp.; In English; Original contains color and black and white illustrationsContract(s)/Grant(s): NAG3-2699; DA Proj. 1L1-61102-AF-20; WBS 848.02.07.03Report No.(s): NASA/TM-2006-214129; ARL-MR-0632; E-15463; No Copyright; Avail.: CASI: A03, Hardcopy

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalmanfilters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints(which may be based on physical considerations) are often neglected because they do not fit easily into the structure of theKalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalmanfilter. The resultant filter truncates the PDF (probability density function) of the Kalman filter estimate at the known constraintsand then computes the constrained filter estimate as the mean of the truncated PDF. The incorporation of state variableconstraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvementis demonstrated via simulation results obtained from a turbofan engine model. The turbofan engine model contains 3 statevariables, 11 measurements, and 10 component health parameters. It is also shown that the truncated Kalman filter may bea more accurate way of incorporating inequality constraints than other constrained filters (e.g., the projection approach toconstrained filtering).Author

Turbofan Engines; Probability Theory; Kalman Filters; Aircraft Engines; Flight SafetySource: NASA