化工学报 ›› 2019, Vol. 70 ›› Issue (S1): 150-157.doi: 10.11949/j.issn.0438-1157.20181069

• 过程系统工程 • 上一篇    下一篇

基于迁移变分自编码器-标签映射的湿式球磨机负荷参数软测量

支恩玮(),闫飞,任密蜂,阎高伟()   

  1. 太原理工大学电气与动力工程学院,山西 太原 030024
  • 收稿日期:2018-09-25 修回日期:2019-01-29 出版日期:2019-03-31 发布日期:2019-04-26
  • 通讯作者: 阎高伟 E-mail:zhienwei_tyut@163.com;yangaowei@tyut.edu.cn
  • 作者简介:<named-content content-type="corresp-name">支恩玮</named-content>(1994—),男,硕士研究生,<email>zhienwei_tyut@163.com</email>|阎高伟(1970—),男,博士,教授,<email>yangaowei@tyut.edu.cn</email>
  • 基金资助:
    国家自然科学基金项目(61450011);国家高技术研究发展计划项目(2013AA102306);山西省自然科学基金项目(2015011052, 201701D221112);山西省科技重大专项(MD 2014-07, 20181102017)

Soft sensor of wet ball mill load parameters based on transfer variational autoencoder - label mapping

Enwei ZHI(),Fei YAN,Mifeng REN,Gaowei YAN()   

  1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • Received:2018-09-25 Revised:2019-01-29 Online:2019-03-31 Published:2019-04-26
  • Contact: Gaowei YAN E-mail:zhienwei_tyut@163.com;yangaowei@tyut.edu.cn

摘要:

在工况改变时,湿式球磨机的实时数据和建模数据分布不一致,不满足传统软测量建模方法要求的数据同分布假设,导致模型失准和性能恶化。为此,引入迁移学习思想,提出一种基于迁移变分自编码器-标签映射的软测量模型,实现多工况下湿式球磨机负荷参数的准确测量。首先,迁移目标域数据编码得到的隐变量分布参数,对源域数据对应隐变量进行拟合,再解码得到迁移数据;然后采用相似性度量选取相似样本构建标签映射模型,并得到映射标签;最后使用迁移数据和映射标签构建出最终的软测量模型。实验结果表明,该软测量方法显著优于现有方法,适用于多工况下的软测量建模。

关键词: 迁移学习, 变分自编码器, 标签映射, 湿式球磨机负荷参数, 过程控制, 预测, 实验验证

Abstract:

When the working condition of wet ball mill is changed, the distributions of real time data and modeling data are inconsistent, and the i.i.d assumption of traditional soft sensor method would not be satisfied, which leads to the inaccuracy of soft sensor model and the deterioration of the system performance. Therefore, by introducing the transfer learning method, a soft sensor model based on transfer variational autoencoder - label mapping strategy is proposed to achieve the precise measurement of wet ball mill load parameters under multiple working conditions. Firstly, the parameters of hidden variables distribution obtained by encode with target domain data are transferred to fit corresponding hidden variable of source domain data. For then, acquiring transfer data by decoding. Moreover, the similarity measure is used to select similar samples to construct the label mapping model, and the mapped labels are obtained. Then the final soft sensor model is constructed according to the transfer data and mapped labels. The experimental results show that the proposed soft sensor method is significantly better than the existing method, and the proposed method is suitable for soft sensor modeling under the situation of multi working condition.

Key words: transfer learning, variational autoencoder, label mapping, wet ball mill load parameters, process control, prediction, experimental validation

中图分类号: 

  • TP 29

图1

变分自编码器"

图2

隐变量z概率密度曲线"

图3

TVAE-LM算法流程"

图4

引入迁移预测结果对比"

图5

引入LM预测结果对比"

表1

引入标签映射预测结果对比(RMSE)"

Working conditionTVAETVAE-LM
MBVRPDCVRMBVRPDCVR
1→20.19610.03450.05430.12360.02660.0164
1→30.35690.04300.11850.13980.02910.0213
3→10.55950.07910.07540.30650.05790.0349
3→20.29470.05940.04500.11810.02910.0157

图6

不同建模方法软测量实验误差比较"

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