Fault Diagnosis Method of Rolling Bearing Combining with SPA Dispersion Entropy and GK Clustering
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摘要: 为充分利用振动信号的特征信息进行故障辨识, 提出一种平滑先验分析(SPA)散布熵和GK聚类相结合的滚动轴承故障诊断方法。首先对滚动轴承振动信号进行SPA分解得到趋势项和波动项; 然后分别计算趋势项和波动项的散布熵值构建特征向量; 最后将特征向量输入至GK分类器中进行聚类识别。将该方法应用到不同工况下的滚动轴承实验数据中, 分析结果表明, 与传统的基于经验模态分解(EMD)散布熵和GK聚类的故障诊断方法相比, 所提方法能够更加准确地实现轴承的故障判别。Abstract: In order to make full use of the characteristic information of vibration signal for fault identification, a rolling bearing fault diagnosis method combining with smoothness priors approach (SPA) dispersion entropy and Gustafson-Kessel (GK) clustering was proposed in this paper. Firstly, the SPA algorithm was used to decompose the vibration signal of rolling bearings, and the trend and de-trend terms was obtained. Secondly, the dispersion entropy of the trend and de-trend was calculated to construct feature vectors. Finally, the feature vectors were input into the GK classifier for clustering and recognized. The proposed method was applied to the experimental data of rolling bearing in different working conditions. The results show that compared with the traditional fault diagnosis methods based on empirical mode decomposition (EMD) dispersion entropy and GK clustering, the proposed method can accurately achieve fault diagnosis of rolling bearings.
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Key words:
- rolling bearing /
- smoothness priors approach /
- dispersion entropy /
- GK clustering /
- fault diagnosis
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表 1 不同λ下趋势项及波动项与原信号的相关系数
λ 3 4 5 6 7 趋势项 0.969 0.973 0.976 0.979 0.981 波动项 0.375 0.333 0.309 0.294 0.283 
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表 2 不同状态信号的趋势项和波动项的散布熵值
信号类型 趋势项 波动项 NR 4.16 3.40 IRF 4.85 3.60 ORF 3.87 3.70 BF 4.63 3.43
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