{"title":"Cross correlation-based blocking whitening slow feature analysis for coupled-data industrial process monitoring","authors":"Jiao Meng, Xin Huo, Hewei Gao, Changchun He","doi":"10.1016/j.jprocont.2025.103461","DOIUrl":null,"url":null,"abstract":"<div><div>Modern industrial systems are characterized by high complexity, strong coupling, and multi-source data interactions. Practical industrial process data are mostly nonlinear, dynamical, and manifested as temporal correlation, which makes it challenging for traditional monitoring methods to accurately capture the changes in internal state variables. To this end, a multi-block whitening slow feature analysis (MBW-SFA) approach is proposed in this paper, which utilizes the cross maximum information coefficient (CMIC) for nonlinear correlation analysis, blocking, as well as selective whitening transformation (SWT) for the nonlinear and strongly coupled process variables, so as to avoid the information loss caused by global whitening. The proposed MBW-SFA approach performs manifold mapping on the data with strong correlations, preserving key information while reducing dimensionality, and the selective whitening transformation is applied to prevent information loss across different variables. In addition, for scenarios involving partially known fault data, this study proposes a control limit optimization (CLO) function that balances fault identification and false alarms to calculate control limit thresholds based on slow feature monitoring statistics, achieving objective monitoring of industrial processes. The proposed approach is experimentally validated on Tennessee Eastman process, where the correlation between process variables is investigated, and the results show that the proposed method achieves excellent performance in process monitoring.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103461"},"PeriodicalIF":3.3000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152425000897","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Modern industrial systems are characterized by high complexity, strong coupling, and multi-source data interactions. Practical industrial process data are mostly nonlinear, dynamical, and manifested as temporal correlation, which makes it challenging for traditional monitoring methods to accurately capture the changes in internal state variables. To this end, a multi-block whitening slow feature analysis (MBW-SFA) approach is proposed in this paper, which utilizes the cross maximum information coefficient (CMIC) for nonlinear correlation analysis, blocking, as well as selective whitening transformation (SWT) for the nonlinear and strongly coupled process variables, so as to avoid the information loss caused by global whitening. The proposed MBW-SFA approach performs manifold mapping on the data with strong correlations, preserving key information while reducing dimensionality, and the selective whitening transformation is applied to prevent information loss across different variables. In addition, for scenarios involving partially known fault data, this study proposes a control limit optimization (CLO) function that balances fault identification and false alarms to calculate control limit thresholds based on slow feature monitoring statistics, achieving objective monitoring of industrial processes. The proposed approach is experimentally validated on Tennessee Eastman process, where the correlation between process variables is investigated, and the results show that the proposed method achieves excellent performance in process monitoring.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.