An Optimal Orthogonal Decomposition Method for Kalman Filter-Based Turbofan Engine Thrust Estimation

Abstract

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 on 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 that 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.

The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring

“…A second one is the selection of the subset of the most observable health parameters given a sensor configuration in order to have a square problem (m = n). This problem has already been investigated within the scope of thrust estimation in [22]. The reduction is however carried out in a transformed space, so that the health parameters become actually tuners which turn out to be of little interest for diagnostics purpose.…”

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

“…When a component is faulty, it is important to know the engine's performance parameters such as thrust, compressor stall margins, and combustor temperature [13,14]. By knowing these parameters, the engine could be controlled within its operability limits while providing the thrust requested by the pilot.…”

Automatic segmentation and prognostic method of a turbofan engine using manifold learning and spectral clustering algorithms

“…[1][2][3][4][5] The performance of an aircraft's engine deteriorates when it is operated because its components physically degrade. To comprehensively describe the failure evolution, the degradation states before failure should be paid more attention.…”