Multi-Source Domain Adaptation Method of Mill Load Based on Common and Special Characteristics

In the grinding industry, accurate prediction of the mill load is the key to increasing mill income and reducing mill failure. It is difficult to improve the prediction accuracy of the model due to insufficient information on single-source domain data and distribution differences among different data. A multi-source domain unsupervised domain adaptation method based on common and special features is proposed. Multi-source domain data has both common and special characteristics. If only common features are emphasized, some useful information will be discarded. If only special features are used, the model generalization is not good. To solve this problem, a common feature extraction block is used to extract the common domain invariant representation of multiple source domains and target domains, and special features are obtained through the special feature extraction block. After the features are fused and input into the common regressor, the multi-source domain predicted values are obtained. Finally, the predicted values of multiple source domains are added and averaged to get the final prediction result. The effectiveness of this method is proved by cross-experiments on the ball mill data set collected in the laboratory.