{"title":"Monitoring dynamic process with orthonormal subspace analysis","authors":"Zhijiang Lou, Weichen Hao, Shan Lu, Yonghui Wang","doi":"10.1002/cjce.25242","DOIUrl":null,"url":null,"abstract":"<p>Traditional multivariate statistics-based process monitoring (MSPM) methods are static algorithms, and the “time lag shift” method (TLSM) is the most commonly used approach to handle the dynamic issue. This paper proves in theory that two drawbacks exist in TLSM-based dynamic approaches: information unrelated to the real-time data is also analyzed, and information that can be predicted by historical data is counted repeatedly in both real-time and historical data. This paper adopts orthonormal subspace analysis (OSA) to handle these issues. OSA can successfully separate real-time data into information that can be predicted by historical data (the dynamic component) and cannot be predicted for process monitoring (the static component), so the detection result is not influenced by redundant information and is more sensitive to process faults than TLSM-based dynamic methods.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cjce.25242","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Traditional multivariate statistics-based process monitoring (MSPM) methods are static algorithms, and the “time lag shift” method (TLSM) is the most commonly used approach to handle the dynamic issue. This paper proves in theory that two drawbacks exist in TLSM-based dynamic approaches: information unrelated to the real-time data is also analyzed, and information that can be predicted by historical data is counted repeatedly in both real-time and historical data. This paper adopts orthonormal subspace analysis (OSA) to handle these issues. OSA can successfully separate real-time data into information that can be predicted by historical data (the dynamic component) and cannot be predicted for process monitoring (the static component), so the detection result is not influenced by redundant information and is more sensitive to process faults than TLSM-based dynamic methods.
期刊介绍：
The Canadian Journal of Chemical Engineering (CJChE) publishes original research articles, new theoretical interpretation or experimental findings and critical reviews in the science or industrial practice of chemical and biochemical processes. Preference is given to papers having a clearly indicated scope and applicability in any of the following areas: Fluid mechanics, heat and mass transfer, multiphase flows, separations processes, thermodynamics, process systems engineering, reactors and reaction kinetics, catalysis, interfacial phenomena, electrochemical phenomena, bioengineering, minerals processing and natural products and environmental and energy engineering. Papers that merely describe or present a conventional or routine analysis of existing processes will not be considered.
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