摘要
近年来,变分自编码器(Variational auto-encoder,VAE)模型由于在概率数据描述和特征提取能力等方面的优越性,受到了学术界和工业界的广泛关注,并被引入到工业过程监测、诊断和软测量建模等应用中.然而,传统基于VAE的软测量方法使用高斯分布作为潜在变量的分布,限制了其对复杂工业过程数据,尤其是多模态数据的建模能力.为了解决这一问题,本论文提出了一种混合变分自编码器回归模型(Mixture variational autoencoder regression,MVAER),并将其应用于复杂多模态工业过程的软测量建模.具体来说,该方法采用高斯混合模型来描述VAE的潜在变量分布,通过非线性映射将复杂多模态数据映射到潜在空间,学习各模态下的潜在变量,获取原始数据的有效特征表示.同时,建立潜在特征表示与关键质量变量之间的回归模型,实现软测量应用.通过一个数值例子和一个实际工业案例,对所提模型的性能进行了评估,验证了该模型的有效性和优越性.
Recently,variational autoencoder(VAE)has caught much attention from academia and industry owing to its superiority in probabilistic data description and feature extraction,and has been introduced into industrial applications such as process monitoring,diagnosis and soft sensor modeling.However,traditional soft sensing methods based on VAE use the Gaussian distribution as the distribution of latent variables,which limits their ability to model complex industrial process data,especially multimode data.To tackle this issue,a mixture variational autoencoder regression(MVAER)model is proposed and applied to soft sensor modeling for complex multimode industrial processes in this paper.Specifically,the proposed model maps multimode data to the latent space by nonlinear mapping and uses the Gaussian mixture model to describe the distribution of latent variables.Thus,the latent variables under each mode are learned to obtain the effective feature representation of the original data.Meanwhile,a regression model between latent features and key quality variables is established for soft sensor application.Case studies including a numerical example and a real-world industrial process are carried out to assess the performance of the MVAER model,which demonstrate the effectiveness and superiority of the proposed approach.
作者
崔琳琳
沈冰冰
葛志强
CUI Lin-Lin;SHEN Bing-Bing;GE Zhi-Qiang(Institute of Industrial Process Control,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027;State Key Laboratory of Industrial Control Technology,Zheji-ang University,Hangzhou 310027)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2022年第2期398-407,共10页
Acta Automatica Sinica
基金
国家自然科学基金(62103362,92167106)
浙江省自然科学基金(LR18F030001)资助。
关键词
软测量
变分自编码器
高斯混合模型
混合变分自编码器回归模型
多模态工业过程
Soft sensor
variational autoencoder
Gaussian mixture model
mixture variational autoencoder regression model
multimode industrial process
