A fault feature extraction model based on nonlinear manifold learningJIANG Quan-sheng1 LI Hua-rong1 HUANG Peng2

JIANG Quan-sheng LI Hua-rong HUANG Peng

Journal of Vibration and Shock ›› 2012, Vol. 31 ›› Issue (23) : 132-136.

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PDF(1562 KB)
Journal of Vibration and Shock ›› 2012, Vol. 31 ›› Issue (23) : 132-136.
论文

A fault feature extraction model based on nonlinear manifold learningJIANG Quan-sheng1 LI Hua-rong1 HUANG Peng2

  • JIANG Quan-sheng1 LI Hua-rong1 HUANG Peng2
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Abstract

Manifold learning is one of the effective methods as obtaining the geometric distribution within the high-dimensional nonlinear dataset, which can be used for fault signal feature extraction and diagnosis. To carry out the diagnostic problem of nonlinear, complex failure symptom in the mechanical fault diagnosis, we propose a feature extraction model based on manifold learning method. In the model, aiming to different processing conditions of the collected sample, the Laplacian Eigenmaps and its incremental, supervision algorithm are applied to implementing the feature extraction and classification to fault sample. As a result of the non-linear dimension reduction method, the model greatly retained the overall geometry information in the fault signal, which significantly enhanced the classification performance of fault pattern recognition. The experimental results for fault diagnosis of air compressor demonstrate the feasibility and effectiveness of the proposed model.

Key words

nonlinear manifold learning / feature extraction / fault diagnosis / Laplacian Eigenmaps

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JIANG Quan-sheng LI Hua-rong HUANG Peng. A fault feature extraction model based on nonlinear manifold learningJIANG Quan-sheng1 LI Hua-rong1 HUANG Peng2[J]. Journal of Vibration and Shock, 2012, 31(23): 132-136
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