Compressor stall warning using nonlinear feature extraction algorithms

Abstract

The present disclosure relates to a novel method to detect an imminent compressor stall by using nonlinear feature extraction algorithms. The present disclosure focuses on the small nonlinear disturbances prior to deep surge and introduces a novel approach to identify these disturbances using nonlinear feature extraction algorithms including phase-reconstruction of time-serial signals and evaluation of a parameter called approximate entropy. The technique is applied to stall data sets from a high-speed centrifugal compressor that unexpectedly entered rotating stall during a speed transient and a multi-stage axial compressor with both modal- and spike-type stall inception. In both cases, nonlinear disturbances appear, in terms of spikes in approximate entropy, prior to surge. The presence of these pre-surge spikes indicates imminent compressor stall.

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