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Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization
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Mien, Van and Hee-Jun, Kang (2016) Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization. IEEE Transactions on Industrial Informatics, 12 (1). pp. 124-135. doi:10.1109/TII.2015.2500098 ISSN 1551-3203.
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Official URL: http://dx.doi.org/10.1109/TII.2015.2500098
Abstract
In order to enhance the performance of bearing defect classification, feature extraction and dimensionality reduction have become important. In order to extract the effective features, wavelet kernel local fisher discriminant analysis (WKLFDA) is first proposed; herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. In order to automatically select the parameters of WKLFDA, a particle swarm optimization (PSO) algorithm is employed, yielding a new PSO-WKLFDA. When compared with the other state-of-the-art methods, the proposed PSO-WKLFDA yields better performance. However, the use of a single global transformation of PSO-WKLFDA for the multiclass task does not provide excellent classification accuracy due to the fact that the projected data still significantly overlap with each other in the projected subspace. In order to enhance the performance of bearing defect classification, a novel method is then proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy. Then, individual PSO-WKLFDA (I-PSO-WKLFDA) is used for extracting effective features of each binary class. The extracted effective features of each binary class are input to a support vector machine (SVM) classifier. Finally, a decision fusion mechanism is employed to merge the classification results from each SVM classifier to identify the bearing condition. Simulation results using synthetic data and experimental results using different bearing fault types show that the proposed method is well suited and effective for bearing defect classification.
| 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 Industrial Informatics | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 1551-3203 | ||||||
| Official Date: | 3 February 2016 | ||||||
| Dates: |
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| Volume: | 12 | ||||||
| Number: | 1 | ||||||
| Page Range: | pp. 124-135 | ||||||
| DOI: | 10.1109/TII.2015.2500098 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Adapted As: |
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