Paper Title:
Machine Performance Degradation Recognition Using Locality Preserving Projections and Clustering Approach
  Abstract

The sensitivity of various features that are characteristics of machine performance may vary significantly under different working conditions. Thus it is critical to devise a systematic feature extraction (FE) approach that provides a useful and automatic guidance on using the most effective features for machine performance recognition without human intervention. This paper proposes a locality preserving projection (LPP)-based FE approach for machine performance degradation recognition. Different from principal component analysis (PCA) that aims to discover the global structure of the Euclidean space, LPP is capable to discover local structure of the data manifold. This may enable LPP to find more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. This experimental result on a bearing test-bed shows that LPP-based FE improves the performance of recognizers for identifying performance degradation of bearings.

  Info
Periodical
Advanced Materials Research (Volumes 443-444)
Chapter
Chapter 2: Optical, Electronic Materials and Industrial Application
Edited by
Li Jian
Pages
929-934
DOI
10.4028/www.scientific.net/AMR.443-444.929
Citation
J. B. Yu, J. P. Liu, M. F. Liu, J. T. Yin, Y. G. Wang, "Machine Performance Degradation Recognition Using Locality Preserving Projections and Clustering Approach", Advanced Materials Research, Vols. 443-444, pp. 929-934, 2012
Online since
January 2012
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Price
$32.00
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