文献类型：期刊文献

英文题名：Distribution Adaptation Local Outlier Factor for Multimode Process monitoring

作者：Xiao, Yutang[1]; Tao, Yang[1]; Shi, Hongbo[1]

机构：[1] East China University of Science and Technology, Key Lab. of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, 130 Meilong Road, Shanghai, 200237, China

年份：2020

卷号：2020-July

起止页码：2294

外文期刊名：Chinese Control Conference, CCC

收录：EI(收录号：20203909241484)

语种：英文

外文关键词：Multivariant analysis - Statistical process control - Statistics

摘要：In modern industrial processes, the production process includes multiple operating modes, due to changes in production goals and conditions. And the data generated in this process is a mixture of Gaussian and non-Gaussian distributions. Therefore, the data distribution of multimode processes is uncertain and complex. It is difficult to monitor multimode processes accurately by using traditional Multivariate Statistical Process Monitoring (MSPM) methods. In this case, the absence of labeled data is considered more valuable. This paper proposes a new unsupervised domain adaptive method called the Distribution Adaptation Local Outlier Factor (DALOF). This method can extract useful information from multimode data through domain adaptation, which can reduce the distribution difference of training data and improve the accuracy of modeling. In the DALOF model, data from different modes in the training set are defined as the source domain and the target domain. Weight the training data according to their correlation, and then project these data into a low-dimensional subspace. In this space, the distribution distance between the source and target domain is minimum. To solve the nonlinear characteristics of multimode data, this paper uses LOF to build a model and formed a density-based monitoring index. Numerical examples and Tennessee Eastman (TE) process simulation demonstrate the effectiveness of DALOF. ? 2020 Technical Committee on Control Theory, Chinese Association of Automation.