Intelligent Identification of Rotating Stall for Centrifugal Compressor based on Pressure Pulsation Signals and SDKAE Network

Abstract

Most accidents of centrifugal compressors are caused by fluid pulsation or unsteady fluid excitation. Rotating stall, as an unstable flow phenomenon in the compressor, is a difficult point in the field of fluid machinery research. In this paper, A stack denoising kernel autoencoder neural network method is proposed to study the early warning of rotating stall in a centrifugal compressor. By collecting the pressure pulsation signals of the centrifugal compressor under different flow rates in engineering practice, a double hidden layer sparse denoising autoencoder neural network is constructed. According to the output labels of the network, it can be judged whether the rotation stall occurs. At the same time, the Gaussian kernel is used to optimize the loss function of the whole neural network to improve the signal feature learning ability of the network. From the experimental results, it can be seen that the flow state of the centrifugal compressor is accurately judged, and the rotation stall early warning of the centrifugal compressor at different speeds is realized, which lays a foundation for the research of intelligent operation and maintenance of the centrifugal compressor.