| 蔡改贫,肖文聪,黄耀锋.领域对抗与分类差异的变工况球磨机负荷识别[J].电子测量与仪器学报,2023,37(12):67-75 |
| 领域对抗与分类差异的变工况球磨机负荷识别 |
| Load identification of ball mill under off-design conditions based on domain confrontation and classification difference |
| |
| DOI： |
| 中文关键词: 迁移学习 领域对抗 分类差异 域适应 负荷识别 |
| 英文关键词:transfer learning domain confrontation classification difference domain adaptation load identification |
| 基金项目:国家自然科学基金(52364025)、江西省科技厅重点研发计划项目(20181ACE50034)资助 |
|
|
|
|
| 摘要点击次数: 264 |
| 全文下载次数: 405 |
| 中文摘要: |
| 在变工况球磨机负荷识别过程中,针对域适应方法在源域和目标域的特征迁移中没有考虑目标域样本而导致域适应效
果不好的问题,本文提出一种基于领域对抗与分类差异的域适应方法。 该方法使用域对抗训练方式实现源域和目标域之间的
特征的对齐;同时,引入两个分类器用于检测远离目标域中的样本,利用最大化和最小化分类器之间的不一致性,实现目标域和
源域特征的自适应匹配,达到更好的域适应效果。 为了验证训练分类器误差的方法能够考虑类内边界提高目标域上的负荷识
别准确率,设计了迁移实验分析其差异损失函数对模型迁移性能的影响,实验表明,当分类器损失值大于 0. 02 时预测模型的准
确率会下降 0. 8% ~ 1. 2%,且较未引入分类器差异损失模型的负荷精度高,可达到 95. 78%。 通过与两类经典的迁移方法进行
对比,验证了该方法在变工况下磨机负荷识别应用中的优势。 |
| 英文摘要: |
| In the process of load identification of ball mill under varying working conditions, a domain adaptation method based on
domain antagonism and classification difference is proposed to solve the problem that domain adaptation method does not consider the
target domain sample in the feature transfer between source domain and target domain. The method uses domain adversarial training to
align the features between source domain and target domain. At the same time, two classifiers are introduced to detect samples far away
from the target domain, and the inconsistency between the maximization and minimization of the classifiers is utilized to realize the
adaptive matching of the features of the target domain and the source domain to achieve a better domain adaptation effect. In order to
verify that the method of training the classifier error can consider the in-class boundary to improve the load recognition accuracy on the
target domain, a migration experiment is designed to analyze the impact of its difference loss function on the model migration
performance. The experiment shows: When the classifier loss value is greater than 0. 02, the accuracy of the prediction model will
decrease by 0. 8% ~ 1. 2%, and the load accuracy is higher than that of the model without the classifier differential loss, which can reach
95. 78%. Compared with two classical transfer methods, the advantages of this method in the application of mill load identification under
varying working conditions are verified. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
