Soft measurement of ball mill load under variable working conditions based on deep transfer learning

Abstract

In actual industrial production, the labeled sample data of the ball mill is difficult to be obtained under variable working condition. Aiming at realizing the soft measurement of ball mill load under variable working conditions, a joint discriminative high-order moment alignment network (JDMAN) is proposed based on the deep transfer learning in this paper. In this method, the discriminative features were learned through jointly training the labeled samples belonging to source domain and unlabeled samples belonging to target domain. Simultaneously, the features learned by deep convolution network were aligned and clustered through central moment discrepancy (CMD) and center loss to accomplish transfer. The comparison with other transfer methods indicate that the proposed JDMAN can efficaciously promote the accuracy of soft measurement under variable working conditions.