基于改进FOA算法优化WKELM的医疗滚动轴承故障诊断技术研究

针对传统小波核极限学习机（Extreme Learning Machine-ELM）应用于医疗滚动轴承故障诊断中识别精度不高且训练速度慢的一系列问题的出现，并针对性的想出一种更好的对滚动转轴发生的故障进行识别的办法,通过对小波核极限学习机算法进行改进的方法。该方法运用改进果蝇算法（LGMS-Fruit-flying Optimization Algorithm, LGMS-FOA）优化小波核极限学习机中的正则化系数和小波核函数中的参数。采用的方法是变分模态分解（Variational Mode Decomposition-VMD），通过这种方法能够对滚动轴承的故障信号分解为含有故障信息的各模态分量从而提取到故障特征。通过与其他三种算法的实验结果对比证明，基于LGMS-FOA-WKELM的滚动轴承故障诊断方法的识别精度更高且训练时间更短。

Rolling Bearings Fault diagnosis research based on WKELM Optimized by Improved FOA Algorithm

he regularization coefficient and the parameters in the wavelet kernel function of the rolling bearing fault diagnosis based on the extreme learning machine will affect the classification effect of WKELM. A fault identification method based on improved FOA algorithm to optimize WKLEM parameters is proposed. The method uses VMD to decompose the fault signal of the rolling bearing to obtain the modal components containing the fault information, and uses SVD to obtain each modal singular value as the feature vector. The LGMS improved FOA algorithm was introduced to optimize the relevant parameters of WKELM, and the optimal rolling bearing fault diagnosis classifier was constructed. The experimental comparison results show that the LGMS-FOA-WKELM method not only has high recognition accuracy, but also has shorter training time and stronger stability.