Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM

Abstract:

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To enhance the effect of fault diagnosis, a new fualt diagnosis method based on supervised incremental local tangent space alignment (SILTSA) and support vector machine (SVM) is proposed. The supervised learning approach is embedded into the incremental local tangent space alignment algorithm, to realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machines. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods

Info:

Periodical:

Edited by:

Shengyi Li, Yingchun Liu, Rongbo Zhu, Hongguang Li, Wensi Ding

Pages:

1233-1237

DOI:

10.4028/www.scientific.net/AMM.34-35.1233

Citation:

G. B. Wang et al., "Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM", Applied Mechanics and Materials, Vols. 34-35, pp. 1233-1237, 2010

Online since:

October 2010

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Price:

$35.00

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