.png){kind=icon}
返回
Novel Virtual Sample Generation Using Target-Relevant Autoencoder for Small Data-Based Soft Sensor
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2021.3120135.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the complex industrial processes, the process data have the characteristics of imbalance and are incomplete due to the difficult-to-measure key variables, leading to the performance degradation of soft sensors. In order to deal with this problem, a novel noise injection virtual sample generation approach based on target-relevant autoencoder (NITAE-VSG) is proposed in this article. Different from the conventional VSG methods, the presented NITAE-VSG model can generate useful input and output virtual samples directly. To increase the diversity of virtual samples, the Gaussian noise (G_noise) is added into the features extracted from the target-relevant autoencoder (TAE) hidden layer. The generated virtual samples are combined with the original small samples to enhance the soft sensor model performance. To verify the reliable feasibility and superior effectiveness of the presented NITAE-VSG, two actual industrial cases: an industrial process of purified terephthalic acid shortened as PTA and a production system of ethylene are carried out. The results of simulations demonstrate that the presented NITAE-VSG can generate superior virtual samples to some other virtual sample generation (VSG) methods and can effectively improve the soft sensor modeling accuracy.
Keyword:
Industrial processes
small sample
soft sensor
target-relevant autoencoder (TAE)
virtual sample generation (VSG)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
Novel manifold learning based virtual sample generation for optimizing soft sensor with small data基于流形学习的小数据量软测量优化虚拟样本生成方法.png)
ISA TRANSACTIONS
ISA TRANSACTIONSIF6.5
PREAI
分享收藏A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industriesAPPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏Gray bootstrap method for estimating frequency-varying random vibration signals with small samples估计小样本时变随机振动信号的灰色自举方法
OAAI
OAAI分享收藏A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industryENERGY
ENERGYIF9.4
PREAI
分享收藏A Layer-Wise Data Augmentation Strategy for Deep Learning Networks and Its Soft Sensor Application in an Industrial Hydrocracking Process深度学习网络的分层数据增强策略及其在工业加氢裂化过程中的软测量应用
PREAI
分享收藏
查看更多内容