Ge Jianghua, Niu Tianyu, Xu Di, Yin Guibin, Wang Yaping
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.
Entropy (Basel). 2020 Mar 2;22(3):290. doi: 10.3390/e22030290.
Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.
特征提取是故障诊断中具有挑战性的问题之一，它直接关系到故障诊断的准确性。因此，本文提出了一种基于集成经验模态分解（EEMD）、小波半软阈值（WSST）信号重构和多尺度熵（MSE）的新方法。首先，应用EEMD方法将振动信号分解为固有模态函数（IMF），然后通过皮尔逊相关系数筛选出包含更多噪声信息的高频IMF。接着，应用WSST方法对信号的高频部分进行去噪以重构信号。其次，应用MSE方法计算重构信号的MSE值，构建具有复杂度度量的特征向量。最后，将特征向量输入支持向量机（SVM）以获得故障诊断结果。实验结果表明，所提方法具有较好的分类性能，能够更好地解决高频信号中有效信号与噪声混合的问题。基于所提方法，能够准确识别故障类型，平均分类准确率达100%。
