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| Soft measurement for a ball mill load parameters based on integration of semi-supervised multi-source domain adaptation |
| LI Sisi1,2， YAN Gaowei1， YAN Fei1， CHENG Lan1， DU Yonggui1 |
1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
2. Shanxi Institute of Technology, Yangquan 045000, China |
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Abstract Aiming at the model mismatch problem caused by distribution difference between historical data and data to be measured after changes of a ball mill’s working conditions and the problem of less samples of working conditions to be measured, a soft measurement method for ball mill load parameters based on semi-supervised domain adaptation was studied here. Considering effects of output label on the characteristic transform matrix, firstly constraint conditions were integrated to search the characteristic transform matrix, and historical data anddata to be measured were projected into the common subspace. Then, a regression model was established according to the projected historical data and less labeled data to be measured to obtain load parameters of unlabeled data to be measured. Considering different working conditions’ historical data having information complementation feature, a soft measurement model based on integration of semi-supervised multi-source domain adaptation was built to further improve the correctness of soft measurement model. The measured data of multi-working condition tests of ball mills in laboratory showed that the proposed method can effectively improve the prediction accuracy of ball mill load parameters.
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Received: 16 January 2018
Published: 28 September 2019
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