.png){kind=icon}
返回
Dynamic transfer soft sensor for concept drift adaptation?
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jprocont.2023.01.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data-driven soft sensor technology has been widely used in process monitoring, quality prediction, etc. However, there are dynamic time-varying and concept drift problems in industrial processes, making the accurate modeling of the soft sensor is still a challenging task. To solve the above problems, this paper proposes a concept drift adaptive dynamic partial least squares method. The method maps highdimensional process data into a low-dimensional latent variable subspace. Dynamic information is introduced by establishing the dynamic regression relationship between process latent variables and quality latent variables. At the same time, transfer learning is used to align second-order statistics between latent variables of data from different distributions to solve the concept drift problem. The experiments on multiple industrial datasets show that the method proposed in this paper can effectively reduce prediction errors and improve the generalization ability of the model. (c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Available online xxxx
Soft sensor
Concept drift
Transfer learning
Dynamic partial least squares
期刊
IF:
3.9
论文数:
3.5K
被引数:
7.3K
机构
引用论文
Double locally weighted principal component regression for soft sensor with sample selection under supervised latent structure具有监督潜在结构下样本选择的软测量双局部加权主成分回归
PREAI
分享收藏A novel recurrent neural network soft sensor via a differential evolution training algorithm for the tire contact patch.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysisAUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
查看更多内容