基于实例迁移的磨煤机过程监测建模

当工业生产过程数据匮乏时，很难利用基于数据统计的方法建立其过程监测模型，这给过程监测的准确性和及时性带来很大影响，迁移学习为解决上述问题提供了有效的途径.针对目标域磨煤机过程数据较少的情况，在源域磨煤机数据的基础上，建立基于实例迁移高斯混合模型(Gaussian mixture model，GMM)的目标域磨煤机过程监测模型.利用实例迁移对源域生产过程和目标域过程数据进行权重分配，通过改进的高斯混合模型算法得到最佳高斯组分数目和对应的模型参数，应用过程监测的全局概率指标实现磨煤机过程的跨域监测.磨煤机过程的研究结果验证了所提出方法的可行性和有效性.

Modeling of Coal Mill Process Monitoring Based on Instance-based Transfer Learning

Considering the shortage of industrial process data， process monitoring model based on data statistics is difficultly established， resulting in an adverse impact on the accuracy and timeliness of monitoring. Transfer learning provides an effective way for the above situation. In view of the fact that the coal mill process data in the target domain is less， on the basis of the source domain coal mill data， a target domain coal mill process monitoring model based on the instance-based transfer Gaussian mixture model(GMM)is established. The instance-based transfer learning is used to assign weight of source domain production process and target domain process data， using the modified algorithm of GMM to automatically optimize the number of Gaussian components and corresponding model parameters. The global probability index of the process monitoring is applied to realize the cross-domain monitoring of the coal mill process. The research results of the coal mill process verify the feasibility and effectiveness of the proposed method.