Paper Title:
Combination of ICA and SOM for Classification of Machine Condition Patterns
  Abstract

Nonlinear independent component analysis (NICA) is a powerful method for analyzing nonlinear and nongaussian data. Artificial neural network (ANN), especially self-organizing map (SOM) based on unsupervised learning, is an excellent tool for pattern clustering and recognition. A novel multi-NICA network is proposed for feature extraction of different mechanical patterns, followed by a typical ANN that is one of Multi-Layer Perceptron (MLP), or Radial Basis Function Network (RBFN), or self-organizing map (SOM), which implements the final classification. Using NICA and appropriate strategies for further feature extraction, nonlinear and higher than second order features embedded in multi-channel vibration measurements can be captured effectively. Mechanical fault patterns can be recognized correctly. Results from the contrast classification experiments show that the new compound ICA-SOM classifier can be constructed in a simpler way and it can classify various fault patterns with high accuracy, both of which imply a great potential in health condition monitoring of machine systems.

  Info
Periodical
Key Engineering Materials (Volumes 295-296)
Edited by
Yongsheng Gao, Shuetfung Tse and Wei Gao
Pages
643-648
DOI
10.4028/www.scientific.net/KEM.295-296.643
Citation
S. X. Yang, W.D. Jiao, Z.T. Wu, "Combination of ICA and SOM for Classification of Machine Condition Patterns", Key Engineering Materials, Vols. 295-296, pp. 643-648, 2005
Online since
October 2005
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Price
$32.00
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