Dual reduced kernel extreme learning machine for aero-engine fault diagnosis

In order to improve the sparsity of kernel-based extreme learning machine (KELM), this paper proposed a novel method named dual reduced kernel extreme learning machine (DR-KELM). The proposed algorithm incorporates traditional greedy forward learning algorithm into backward learning algorithm to gai...

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Bibliographic Details
Published inAerospace science and technology Vol. 71; pp. 742 - 750
Main Authors Lu, Feng, Jiang, Jipeng, Huang, Jinquan, Qiu, Xiaojie
Format Journal Article
LanguageEnglish
Published Elsevier Masson SAS 01.12.2017
Subjects
Aero-engine
Backward learning
Fault diagnosis
Forward learning
Kernel extreme learning machine
Sparsity
Fault diagnosis
Aero-engine
Forward learning
Sparsity
Backward learning
Kernel extreme learning machine
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Summary:In order to improve the sparsity of kernel-based extreme learning machine (KELM), this paper proposed a novel method named dual reduced kernel extreme learning machine (DR-KELM). The proposed algorithm incorporates traditional greedy forward learning algorithm into backward learning algorithm to gain more sparsity and enhance testing time further. Compared to original KELM, the proposed method produces satisfactory performance of pattern recognition with fewer nodes, and reduces diagnostic consuming time from the tests on benchmark dataset. The DR-KELM application to aero-engine fault diagnosis also demonstrates its superior performance with more sparse structure.
ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2017.10.024