基于最大均值差异多源域迁移学习的 湿式球磨机负荷参数软测量

针对湿式球磨机工况改变时,实时数据与建模数据分布不一致,不满足数据同分布的假设,传统软测量模型难以适应数据分布变化,造成模型性能恶化的问题,有针对性地引入迁移学习策略,并通过多源域集成机制提高模型的鲁棒性,实现多工况下湿式球磨机负荷参数测量.首先,对多工况数据进行预处理并提取频谱特征,经过联合分布适配对多工况数据进行边缘、条件分布适配;然后,使用最大均值差异对适配后的数据进行分布度量并为源域构建的回归器加权;最后,对目标域数据进行负荷预测.通过对比实验与交叉实验表明了模型的实用性和有效性.

Soft sensor of wet ball mill load based on maximum mean discrepancy multi-source domain transfer learning

When the working condition of a wet ball mill is changed, the distribution of real-time data and modeling data is inconsistent. It is difficult to accurately measure the load parameters by using the traditional soft sensor algorithm based on historical data. Therefore, a transfer learning strategy is introduced, and the robustness of the model is improved by the multi domain mechanism. The process is to preprocess and extract the characteristics of multi working conditions data, and the distribution of the edge and the conditional distribution is obtained by joint distribution fitting. Then the maximum mean discrepancy is used to measure the distribution of adaptive data, and the calculated results are applied to the regression weighted. Finally, the target domain data is used for load forecasting. The practicability and effectiveness of the model are illustrated by comparing experiments and cross experiments.