Paper Title:
Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM
  Abstract

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, X. Q. Zhao, Y. H. He, "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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