Abstract: To suppress judgment one-sidedness in the existing bearing fault diagnosis method, a bearing fault diagnosis method based on Gibbs sampling was proposed. Firstly, the bearing vibration signal was decomposed by Local Characteristic Scale Decomposition (LCD) to obtain Intrinsic Scale Components (ISC). Secondly, the time domain features were extracted from the bearing vibration signal and ISC, and the time domain features were ranked according to feature sensitivity level. The top ranked features were selected to make up feature sets. Thirdly, feature set training was used to generate a multi-dimensional Gaussian distribution model based on Gibbs sampling. Finally, posterior analysis was used to obtain the probability to realize bearing fault diagnosis. The experimental results show that the diagnostic accuracy of the proposed method reaches 100%; compared with the bearing diagnosis method based on SVM (Support Vector Machine), the diagnostic accuracy is improved by 11.1 percentage points when the number of features is 43. The proposed method can effectively diagnose rolling bearing faults, and it also has good diagnostic effect on high-dimensional and complex bearing fault data.

Key words: rolling bearing, fault diagnosis, Local Characteristic scale Decomposition (LCD), multi-dimensional Gaussian distribution, Gibbs sampling

摘要： 针对现有轴承故障诊断方法的不足，即诊断片面性问题，提出了一种基于Gibbs抽样的轴承故障诊断方法。首先对轴承振动信号进行局部特征尺度分解（LCD）得到内禀尺度分量（ISC）；然后对轴承振动信号和ISC分别提取时域特征，按照特征敏感度高低对时域特征排名，选择排名靠前的特征组成特征集；其次使用特征集训练产生基于Gibbs抽样的多维高斯分布模型；最后通过后验分析得到概率，实现轴承故障诊断。实验结果表明诊断正确率达到100%，与基于SVM的轴承诊断方法相比，在特征数为43个时诊断正确率提升了11.1个百分点。所提方法能够有效地对滚动轴承故障状态进行诊断，对高维复杂的轴承故障数据也有很好的诊断效果。

关键词: 滚动轴承, 故障诊断, 局部特征尺度分解, 多维高斯分布, 吉布斯抽样