Journal ArticleDOI
Online monitoring of performance variations and process dynamic anomalies with performance-relevant full decomposition of slow feature analysis
Jiale Zheng,Chunhui Zhao +1 more
TLDR
A performance-relevant full decomposition of slow feature analysis termed PFDSFA is proposed for process monitoring under closed-loop control by simultaneously considering the influences of process variations on process performance and dynamics and achieves comprehensive process monitoring of process static and dynamic characteristics.About:
This article is published in Journal of Process Control.The article was published on 2019-08-01. It has received 30 citations till now. The article focuses on the topics: Process variable & Process (computing).read more
Citations
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Journal ArticleDOI
Key-performance-indicator-related state monitoring based on kernel canonical correlation analysis
TL;DR: In this paper, two improved kernel canonical correlation analysis (KCCA) methods are proposed to deal with key performance indicators (KPI)-related issue, and fault detectability analysis and computational complexity analysis on these two methods are performed.
Journal ArticleDOI
Information concentrated variational auto-encoder for quality-related nonlinear process monitoring
TL;DR: A novel algorithm named information concentrated variational auto-encoder (IFCVAE) is proposed, which aims to extract the latent variables which represent both process information and quality information from the quality-related and unrelated aspects of industrial processes.
Journal ArticleDOI
Enhanced canonical variate analysis with slow feature for dynamic process status analytics
TL;DR: A new data-driven algorithm called enhanced canonical variate analysis with slow feature (ECVAS) and corresponding monitoring strategy are proposed for dynamic process monitoring, which achieves in-depth understanding of process dynamics under control actions and helps identify normal changes in operating conditions.
Journal ArticleDOI
Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence
TL;DR: In this article , the authors analyzed the complex characteristics of nonstationary industrial operation, revealed the effects on operating condition monitoring, and summarized the difficulties faced by varying condition monitoring and provided reference for monitoring methods of non-stationary process.
Journal ArticleDOI
Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis
Xinrui Gao,Yuri A.W. Shardt +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a long-term dependency slow feature analysis (LTSFA) to understand the longer-term dynamics by an explicit expression of latent states of the process.
References
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Book ChapterDOI
Relations Between Two Sets of Variates
TL;DR: The concept of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions as discussed by the authors, where the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting.
Journal ArticleDOI
Canonical Correlation Analysis: An Overview with Application to Learning Methods
TL;DR: A general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text and compares orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model is presented.
Journal ArticleDOI
A plant-wide industrial process control problem
James J. Downs,E.F. Vogel +1 more
TL;DR: In this article, a model of an industrial chemical process for the purpose of developing, studying and evaluating process control technology is presented, which is well suited for a wide variety of studies including both plantwide control and multivariable control problems.
Journal ArticleDOI
Orthogonal projections to latent structures (O-PLS)
Johan Trygg,Svante Wold +1 more
TL;DR: In this article, a generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described, which removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity).
Journal ArticleDOI
A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
TL;DR: A penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix, and establishes connections between the SCoTLASS method for sparse principal component analysis and the method of Zou and others (2006).
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