Application of independent component analysis to the aero-engine fault diagnosis

Authors:
Li Zhonghai;Zhang Yan;Jiang Liying;Qu Xiaoguang
Affiliations:
Shenyang Institute of Aeronautical Engineering, Shenyang, China;Shenyang Institute of Aeronautical Engineering, Shenyang, China;Shenyang Institute of Aeronautical Engineering, Shenyang, China;Shenyang Institute of Aeronautical Engineering, Shenyang, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

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Abstract

In view of the complexity of aero-engine system, there are massive correlated parameters, a novel method for aeroengine fault diagnosis based on Independent Component Analysis (ICA) is proposed in this paper. ICA is already widely applied in many domains, but the aeroengine fault diagnosis domain hasn't involved. ICA is used to detect fault by calculating I2 statistics, and SVM models are constructed based on separate matrix of ICA. Applications illustrate the efficiency of the proposed approach.