A Stall Warning Scheme for Aircraft Engines With Inlet Distortion via Deterministic Learning

Abstract

Inlet flow nonuniformity (inlet distortion) can influence the transient behavior of stall disturbances and trigger the compression system instability in aircraft engines. Stall warning provides an approach to avoiding the occurrence of the instability. In this brief, based on the high-order distortion model developed recently, a stall warning scheme employing deterministic learning (DL) algorithm is proposed for aircraft engines with inlet distortion. The high-order distortion model can capture the transient behavior of stall inception for a test rig and be suitable for the analysis of stall warning. First, the system dynamics underlying normal and stall inception are locally accurately approximated through DL. The obtained knowledge of system dynamics is stored in constant radial basis function (RBF) networks. Secondly, a bank of estimators is constructed using the stored constant RBF networks to represent the learning normal and stall inception patterns. By comparing each estimator with a test system, the average $L_{1}$ norms of the residuals are taken as the measure of the dynamical differences between the test system and the learning patterns. The occurrence of stall inception as an early warning signal can be rapidly detected according to the smallest residual principle. Finally, numerical simulation results are given to show the effectiveness of stall warning approach.