Fault diagnosis based on orthogonal semi-supervised LLTSA for feature extraction and Transductive SVM for fault identification

To overcome the low diagnosis accuracy caused by the scarcity of labeled training samples, a fault diagnosis method was proposed using orthogonal Semi-supervised linear local tangent space alignment (OSSLLTSA) for feature extraction and transductive support vector machine (TSVM) for fault identifica...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2018-01, Vol.34 (6), p.3499-3511
Main Authors: Luo, Jiufei, Xu, Haitao, Su, Zuqiang, Xiao, Hong, Zheng, Kai, Zhang, Yi
Format: Article
Language:English
Subjects:
Clustering
Decomposition
Fault diagnosis
Feature extraction
Gearboxes
Machine learning
Manifolds (mathematics)
Statistical analysis
Statistical methods
Support vector machines
Wavelet transforms
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Summary:To overcome the low diagnosis accuracy caused by the scarcity of labeled training samples, a fault diagnosis method was proposed using orthogonal Semi-supervised linear local tangent space alignment (OSSLLTSA) for feature extraction and transductive support vector machine (TSVM) for fault identification. Through extracting the statistical features were extracted from the sub-bands of vibration signals decomposed by wavelet packet decomposition (WPD), the high-dimensional feature set could be obtained. Following that, the improved kernel space distance evaluation method was applied to remove non-sensitive fault features. Then, a semi-supervised manifold learning method (OSSLLTSA) was proposed to reduce the dimensionality of the fault feature set, and thus to extract fused fault features with high clustering performance. OSSLLTSA overcomes the over-learning of supervised manifold learning and projection aimlessness of unsupervised manifold learning. Finally, the low-dimensional feature set after dimension reduction was inputted into TSVM for fault diagnosis. TSVM was able to completely utilize the fault information contained in unlabelled samples to modify the model, and the trained fault diagnosis model has better generalization ability. The effectiveness of the proposed method was verified based on the case of gearbox fault. Experimental results showed that the proposed method is able to achieve very high fault diagnosis accuracy even when labeled samples were insufficient.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-169529