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. 2017 Mar 18;17(3):625.
doi: 10.3390/s17030625.

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

Hongdi Zhou  1 , Tielin Shi  2 , Guanglan Liao  3 , Jianping Xuan  4 , Jie Duan  5 , Lei Su  6 , Zhenzhi He  7 , Wuxing Lai  8
Affiliations

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

Hongdi Zhou et al. Sensors (Basel). .

Abstract

This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

Keywords: Renyi entropy; dimensional reduction; fault diagnosis; feature extraction; weighted kernel entropy component analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Implementation process of the proposed fault diagnosis method.
Figure 2
Figure 2
The test rig.
Figure 3
Figure 3
The time domain and frequency domain figures of vibration signals for the four bearing conditions: (a) normal condition, (b) inner race fault, (c) outer race fault, and (d) ball fault.
Figure 4
Figure 4
The normalized wavelet packet energy and entropy spectrums of the bearing vibration signals under four conditions: (a) normal condition, (b) inner race fault, (c) ball fault, (d) outer race fault.
Figure 5
Figure 5
Feature extraction with PCA: (a) training samples, (b) testing samples.
Figure 6
Figure 6
Feature extraction with KPCA: (a) training samples, (b) testing samples.
Figure 7
Figure 7
Feature extraction with KECA: (a) training samples, (b) testing samples.
Figure 8
Figure 8
Feature extraction with WKECA: (a) training samples, (b) testing samples.
Figure 9
Figure 9
Classification accuracy of SVM based on different feature extraction methods for different labeled samples.
See this image and copyright information in PMC

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