Detection of rotating stall based on deterministic learning

In this paper, deterministic learning theory is used to detect the stall inception signal for the axial compressor. Firstly, based on deterministic learning (DL) theory, the system dynamics underlying normal and stall inception signal are identified and stored in constant radial basis function (RBF) networks. Secondly, through the method of dynamic pattern recognition in DL, the stall inception of the axial compressor could be detected. Simulation results show the validity of the proposed approach.