NDLI: Identification and rapid detection of rotating stall via deterministic learning

Content Provider	IEEE Xplore Digital Library
Author	Wang Cong Peng Tao Chen Tianrui Yuan Hanwen Wang Yong
Copyright Year	2010
Description	Author affiliation: College of Engineering, Peking University, Beijing 100871, China (Wang Yong) \|\| College of Automation and Center for Control and Optimization, South China University of Technology, GuangZhou 510641, China (Wang Cong; Peng Tao; Chen Tianrui; Yuan Hanwen)
Abstract	Rotating stall is a kind of unsteady flow in axial compressors which significantly reduce the performance of turbofan engines. Identification and rapid detection of rotating stall is a very important issue. In this paper, based on the high-order Moore-Greitzer model (the Mansoux model) which captures the transient characteristic of rotating stall, we firstly analyze the properties of the model including the partial diagonal dominance of system dynamics. By using these properties, the locally diagonally dominant system dynamics is identified and stored by RBF networks based on deterministic learning theory. Secondly, the stored knowledge of system dynamics is utilized to achieve rapid detection for rotating stall. Simulation results are included which show that the proposed approach may be useful in modelling and detection of rotating stall and surge.
Starting Page	5926
Ending Page	5931
File Size	510013
Page Count	6
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Analytical models Backstepping Deterministic learning Compressors Rotating stall Identification Electronic mail Moment methods Surges Rapid detection Distributed parameter systems Jet engines
Content Type	Text
Resource Type	Article

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