首页|Early detection of rotating stall in axial flow compressors via deterministic learning:detectability analysis

Early detection of rotating stall in axial flow compressors via deterministic learning:detectability analysis

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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.

Axial compressorRotating stallSurgeFault detectionDeterministic learningDetectability condition

Tianrui Chen、Shuai Han、Zejian Zhu、Cong Wang

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School of Control Science and Engineering,Shandong University,Jinan 250061,Shandong,China

Center for Intelligent Medical Engineering,Shandong University,Jinan 250061,Shandong,China

School of Automation Science and Engineering,South China University of Technology,Guangzhou 510641,Guangdong,China

Major Program of the National Natural Science Foundation of ChinaMajor Basic Program of Shandong Provincial Natural Science Foundation

61890922ZR2020ZD40

2023

10.1007/s11768-023-00137-y

控制理论与技术(英文版)
华南理工大学

控制理论与技术(英文版)

CSCDEI
影响因子：0.307
ISSN：2095-6983
年,卷(期)：2023.21(2)
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