磨粉机磨辊表面磨损特征提取与识别

Extraction and identification of wear features on grinding roller surface of grinding mill

目的:预测磨粉机喷砂辊使用寿命。方法:通过搭建的图像采集系统对磨辊表面的磨损图像进行采集,基于灰度共生矩阵算法,获得磨辊磨损周期内的二阶矩、熵值、对比度和相关性等纹理参数,将获得的纹理特征参数输入构建的基于粒子群算法(PSO)的LS-SVM模型,最终对喷砂辊的磨损寿命进行预测。结果:粒子群算法可以很好地优化LS-SVM的惩罚因子和核参数,PSO-LS-SVM算法要远远优于LS-SVM算法模型,采用PSO-LS-SVM算法可以准确地识别磨粉机喷砂辊表面的磨损状态。结论:该系统可以准确地预测喷砂辊的使用寿命。

Objective:To achieve surface wear life prediction of abrasive blast rollers of grinding machines.Methods:The wear images of the grinding roller surface were acquired by the built image acquisition system,and the texture parameters such as second order moments,entropy value,contrast and correlation in the wear cycle of the grinding roller were obtained based on the grey scale co-generation matrix algorithm,and the obtained texture feature parameters were input into the constructed PSO-based LS-SVM algorithm model to finally predict the wear life of the blast roller.Results:The particle swarm algorithm could optimize the penalty factor and kernel parameters of LS-SVM well,and the PSO-LS-SVM algorithm was far superior to the LS-SVM algorithm model.The wear state of the blast roller surface of the mill could be accurately identified using the PSO-LS-SVM algorithm.Conclusion:The system can accurately predict the service life of the blast rollers.