Early detection of rotating stall in axial flow compressors via deterministic learning:detectability analysis
Tianrui Chen1
Shuai Han1
Zejian Zhu2
Cong Wang1
1.School of Control Science and Engineering,Shandong University,Jinan 250061,Shandong,China;Center for Intelligent Medical Engineering,Shandong University,Jinan 250061,Shandong,China2.School of Automation Science and Engineering,South China University of Technology,Guangzhou 510641,Guangdong,China
摘要：Rotating stall and surge are two violent unstable phenomena of an aero-engine compressor.The early detection of rotating stall is a critical and difficult issue in the operation of a compressor.Recently,a deterministic learning based stall inception detection approach(SIDA)has been developed for modeling and detecting stall inception in aero-engine compressors.This paper considers the derivation of analytical results on the detection capabilities for the SIDA based on deterministic learning.First,by utilizing the input/output stability of the residual system,a detectability condition of the SIDA is presented,and how to choose the parameters of the diagnostic system is also analyzed.Second,based on the relationship between NN approximation capabilities and radial basis function(RBF)network structures,the influence of RBF network structures on the performance properties of the SIDA is analyzed.Finally,a simulation study is presented,in which the Mansoux-C2 compressor model is utilized to verify the effectiveness of the proposed SIDA.
机标关键词：learninganalysisdetectionflowaxialcompressorsdetectabilitydeterministic
论文发表日期：2023-05-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：12( 161-172 )
英文信息