Paper Title:
Supervised Locally Linear Embedding for Fault Diagnosis
  Abstract

Fault diagnosis is a kind of pattern recognition problem and how to extract diagnosis features and improve recognition performance is a difficult problem. Local Linear Embedding (LLE) is an unsupervised non-linear technique that extracts useful features from the high-dimensional data sets with preserved local topology. But the original LLE method is not taking the known class label information of input data into account. A new characteristics similarity-based supervised locally linear embedding (CSSLLE) method for fault diagnosis is proposed in this paper. The CSSLLE method attempts to extract the intrinsic manifold features from high-dimensional fault data by computing Euclidean distance based on characteristics similarity and translate complex mode space into a low-dimensional feature space in which fault classification and diagnosis are carried out easily. The experiments on benchmark data and real fault dataset demonstrate that the proposed approach obtains better performance compared to SLLE, and it is an accurate technique for fault diagnosis.

  Info
Periodical
Advanced Materials Research (Volumes 139-141)
Edited by
Liangchi Zhang, Chunliang Zhang and Tielin Shi
Pages
2599-2602
DOI
10.4028/www.scientific.net/AMR.139-141.2599
Citation
Z. W. Li, R. Nie, Y. F. Han, "Supervised Locally Linear Embedding for Fault Diagnosis", Advanced Materials Research, Vols. 139-141, pp. 2599-2602, 2010
Online since
October 2010
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Price
$32.00
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