Abrupt stall detection for axial compressors with non-uniform inflow via deterministic learning

Abstract

This paper focuses on the early detection problem of abrupt stall inception in axial compressors subject to non-uniform inflow. The considered stall inception is small and abrupt. Such a stall inception is not only easy to cause system instability, but also difficult to be rapidly detected. In this paper, a novel abrupt stall detection approach is proposed for axial compressors with non-uniform inflow by using the deterministic learning algorithm. Firstly, a high-order distortion dynamic mode is derived to generate the normal and stall inception situations. Subsequently, dynamic features of these different situations are extracted and stored in constant radial basis function networks, which are regarded as the training patterns. Based on dynamic pattern recognition, the average L1 norms of the state errors are used as the measure of the dynamical differences between the test pattern and the training patterns, and then a stall detection method is developed to rapidly decide whether the test patterns are the abrupt stall inception. Simulation studies are given to show that the proposed stall detection approach is sensitive to small dynamic changes of axial compressors, thereby rapidly detecting the abrupt stall inception.