Ju Bin, Zhang Haijiao, Liu Yongbin, Liu Fang, Lu Siliang, Dai Zhijia
College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China.
Entropy (Basel). 2018 Mar 21;20(4):212. doi: 10.3390/e20040212.
A feature extraction method named improved multi-scale entropy (IMSE) is proposed for rolling bearing fault diagnosis. This method could overcome information leakage in calculating the similarity of machinery systems, which is based on Pythagorean Theorem and similarity criterion. Features extracted from bearings under different conditions using IMSE are identified by the support vector machine (SVM) classifier. Experimental results show that the proposed method can extract the status information of the bearing. Compared with the multi-scale entropy (MSE) and sample entropy (SE) methods, the identification accuracy of the features extracted by IMSE is improved as well.
提出了一种名为改进多尺度熵（IMSE）的特征提取方法用于滚动轴承故障诊断。该方法基于毕达哥拉斯定理和相似性准则，能够克服在计算机械系统相似性时的信息泄漏问题。使用IMSE从不同工况下的轴承中提取的特征，通过支持向量机（SVM）分类器进行识别。实验结果表明，所提方法能够提取轴承的状态信息。与多尺度熵（MSE）和样本熵（SE）方法相比，IMSE提取的特征的识别准确率也有所提高。
