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dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSun, Ying
dc.contributor.authorHering, Amanda S.
dc.contributor.authorMadakyaru, Muddu
dc.contributor.authorDairi, Abdelkader
dc.date.accessioned2021-03-01T07:08:31Z
dc.date.available2021-03-01T07:08:31Z
dc.date.issued2021
dc.identifier.citationHarrou, F., Sun, Y., Hering, A. S., Madakyaru, M., & Dairi, A. (2021). Nonlinear latent variable regression methods. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches, 119–154. doi:10.1016/b978-0-12-819365-5.00010-3
dc.identifier.isbn9780128193655
dc.identifier.doi10.1016/b978-0-12-819365-5.00010-3
dc.identifier.urihttp://hdl.handle.net/10754/667751
dc.description.abstractDetecting anomalies is crucially important for improving the operation, reliability, and profitability of complex industrial processes. Traditional linear data-driven methods, such as the principal component analysis (PCA) and partial least squares (PLS) method, are extensively exploited for detecting anomalies in multivariate correlated processes. Since most of the data observed in practical applications are innately nonlinear, the development of models able to learn such nonlinearity are indispensable. In this chapter, in order to handle nonlinearity, we use nonlinear latent variable regression (LVR) modeling methods, which are powerful tools for processing nonlinearities. First, we use nonlinear functions using polynomials an adaptive network-based fuzzy-inference system as an inner model of the LVR model (i.e., nonlinear relation between latent variables and output). We then offer a brief overview of nonlinear LVR-based monitoring approaches and how they can be used for anomaly detection. We also present an alternative for dealing with nonlinearities in-process data by using kernel PCA, which captures the nonlinear features in high-dimensional feature spaces via nonlinear kernel functions. Lastly, the methods presented are applied to simulated synthetic data, plug flow reactor data, and real data from a wastewater treatment plant located in Saudi Arabia.
dc.publisherElsevier BV
dc.relation.ispartofDOI:10.1016/c2018-0-05141-5
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/B9780128193655000103
dc.rightsArchived with thanks to Elsevier
dc.titleNonlinear latent variable regression methods
dc.typeBook Chapter
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.rights.embargodate2023
dc.eprint.versionPost-print
dc.contributor.institutionBaylor University, Dept of Statistical Science, Waco, TX, United States.
dc.contributor.institutionDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
dc.contributor.institutionUniversity of Science and Technology of Oran-Mohamed Boudiaf, Computer Science Department, Signal, Image and Speech Laboratory, Oran, Algeria.
dc.identifier.pages119-154
kaust.personHarrou, Fouzi
kaust.personSun, Ying
display.relations<b>Is Part Of:</b><br/> <ul><li><i>[Book]</i> <br/> Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches. (2021). doi:10.1016/c2018-0-05141-5. DOI: <a href="https://doi.org/10.1016/c2018-0-05141-5" >10.1016/c2018-0-05141-5</a> Handle: <a href="http://hdl.handle.net/10754/667757" >10754/667757</a></a></li></ul>


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