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Wavelet kernel local fisher discriminant analysis with particle swarm optimization algorithm for bearing defect classification
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Mien, Van and Kang, H. J. (2015) Wavelet kernel local fisher discriminant analysis with particle swarm optimization algorithm for bearing defect classification. IEEE Transactions on Instrumentation and Measurement, 64 (12). pp. 3588-3600. doi:10.1109/TIM.2015.2450352 ISSN 0018-9456.
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Official URL: http://dx.doi.org/10.1109/TIM.2015.2450352
Abstract
Feature extraction and dimensionality reduction
(DR) are necessary and helpful preprocessing steps for
bearing defect classification. Linear local Fisher discriminant
analysis (LFDA) has recently been developed as a popular method
for feature extraction and DR. However, the linear method
tends to give undesired results if the samples between classes
are nonlinearly separated in the input space. To enhance the
performance of LFDA in bearing defect classification, a new
feature extraction and DR algorithm based on wavelet kernel
LFDA (WKLFDA) is presented in this paper. Herein, a new
wavelet kernel function is proposed to construct the kernel
function of LFDA. To seek the optimal parameters for WKLFDA,
particle swarm optimization (PSO) is used; as a result, a new
PSO-WKLFDA algorithm is proposed. The experimental results
for the synthetic data and measured vibration bearing data show
that the proposed WKLFDA and PSO-WKLFDA outperform
other state-of-the-art algorithms.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Journal or Publication Title: | IEEE Transactions on Instrumentation and Measurement | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 0018-9456 | ||||||
| Official Date: | 6 December 2015 | ||||||
| Dates: |
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| Volume: | 64 | ||||||
| Number: | 12 | ||||||
| Page Range: | pp. 3588-3600 | ||||||
| DOI: | 10.1109/TIM.2015.2450352 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access |
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