基于改进的高斯混合回归的球磨机料位软测量

Soft measurement for ball mill fill level based on improved Gaussian mixture regression

针对球磨机系统多模态复杂过程中的料位不确定性,球磨机振动信号存在非线性、噪声和外界干扰等问题,采用一种基于改进的高斯混合回归(GMR)的球磨机料位软测量方法,解决传统高斯混合模型初始化含有噪声和异常值的数据难以聚类的问题。首先,利用改进的K-medoids聚类算法与EM算法分别初始化和优化高斯混合模型(GMM)的最佳高斯分量个数、最优模型参数,然后采用GMR预测输出球磨机料位。最后实验验证了改进GMR模型得到的预测料位可以很好地跟踪真实料位,并且通过实验结果的对比分析,验证了改进模型的有效性和实用性以及较好的预测精度。

Since the fill level of the ball mill system in multimode complicated process is uncertain,and the vibration signal of ball mill has the characteristics of nonlinearity,noise and outside interference,a soft measurement method for ball mill fill level based on improved Gaussian mixture regression(GMR)is proposed to solve the problem that it is difficult to cluster the data embedding noise and abnormal value of the traditional Gaussian mixture model(GMM) initialization. The improved K-medoids clustering algorithm and EM algorithm are used respectively to initialize and optimize the optimal Gaussian component quantity and optimal model parameters. The GMR is used to predict the output level of the ball mill. The experimental results verify that the predicted fill level obtained by improved GMR model can track the real fill level accurately. The comparative analysis of experimental results verifies that the improved model is feasible and practical,and has high prediction accuracy.