基于LCD与峭度-能量比准则的滚动轴承故障诊断研究

Fault diagnosis of rolling bearing based on LCD and kurtosis-energy ratio criterion

针对局部特征尺度分解在滚动轴承故障诊断中出现未筛选有效分量的问题,通过峭度-能量比准则对LCD分解产生的内禀尺度分量(ISC)进行了筛选,提出了一种基于LCD分解与峭度-能量比准则的方法。首先对采集的滚动轴承振动信号进行了LCD分解,得到了不同能量的ISC分量,运用峭度-能量比准则筛选了有效的ISC分量;再计算了筛选后有效的ISC分量的能量熵和多尺度熵,并将计算的结果融合后构建了特征向量;最后通过支持向量机(SVM)的故障分类器,实现了滚动轴承的故障诊断。实验结果表明:采用峭度-能量比准则提取有效分量减少了冗余分量,滚动轴承内圈故障和外圈故障诊断准确率有了明显提高。

Aiming at the problem of unscreened effective components in the fault diagnosis of rolling bearings in the local feature scale decomposition, the intrinsic scale component(ISC) generated by LCD decomposition was screened through the horn-to-energy ratio criterion. A method based on LCD decomposition and cradle-energy ratio criteria was proposed. First, the collected rolling bearing vibration signal was decomposed by LCD to obtain the ISC components of different energies, and the valid ISC components were screened by using the horn-energy ratio criterion, and the energy entropy and multi-scale entropy of the valid ISC components after screening were calculated. The result of the calculation was combined to construct the feature vector. Finally, the fault diagnosis of the rolling bearing was realized through the support vector machine(SVM) fault classifier. The results indicate that the redundant component of effective component is reduced by using the skew-energy ratio criterion, and the fault diagnosis accuracy of the rolling bearing is obviously improved in inner and outer ring fault diagnosis.