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

Enwei ZHI; Fei YAN; Mifeng REN; Gaowei YAN

doi:10.11949/j.issn.0438-1157.20181069

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

更新时间：2020-06-24

- Soft sensor of wet ball mill load parameters based on transfer variational autoencoder - label mapping
- CIESC Journal Issue S1, Pages: 150-157(2019)
- 作者机构：
  
  太原理工大学电气与动力工程学院，山西太原 030024
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181069
  CLC： TP 29
- Received：25 September 2018，
  
  Revised：2019-01-29，
- 稿件说明：
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支恩玮, 闫飞, 任密蜂, 阎高伟. 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[J]. 化工学报, 2019, 70(S1): 150-157

ZHI Enwei, YAN Fei, REN Mifeng, YAN Gaowei. Soft sensor of wet ball mill load parameters based on transfer variational autoencoder - label mapping[J]. CIESC Journal, 2019, 70(S1): 150-157
支恩玮, 闫飞, 任密蜂, 阎高伟. 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[J]. 化工学报, 2019, 70(S1): 150-157 DOI： 10.11949/j.issn.0438-1157.20181069.

ZHI Enwei, YAN Fei, REN Mifeng, YAN Gaowei. Soft sensor of wet ball mill load parameters based on transfer variational autoencoder - label mapping[J]. CIESC Journal, 2019, 70(S1): 150-157 DOI： 10.11949/j.issn.0438-1157.20181069.

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。

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.

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references

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