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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.accessioned2021-03-01T07:01:11Z
dc.date.available2021-03-01T07:01:11Z
dc.date.issued2021
dc.identifier.citationHarrou, F., Sun, Y., Hering, A. S., Madakyaru, M., & Dairi, A. (2021). Multiscale latent variable regression-based process monitoring methods. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches, 155–191. doi:10.1016/b978-0-12-819365-5.00011-5
dc.identifier.isbn9780128193655
dc.identifier.doi10.1016/b978-0-12-819365-5.00011-5
dc.identifier.urihttp://hdl.handle.net/10754/667749
dc.description.abstractData acquired from industrial processes, usually via sensors, are generally noisy, correlated in time and nonstationary; this makes the implementation of the monitoring process difficult, as most techniques are designed for Gaussian and uncorrelated observations. As conventional monitoring methods, their efficiency may be significantly affected by typical uncertainties in industrial processes. Assumptions of Gaussianity, dependence in time, and stationarity are typically not verified in industrial processes. These properties make wavelet-based fault detection approaches especially appropriate. Wavelet methods are also helpful when the characteristics of the fault are unknown. This chapter discusses wavelet-based monitoring approaches that are flexible and designed with fewer structural assumptions. In this chapter, we present a brief overview of wavelets and their desirable characteristics, as well as the discrete wavelet transform. We then assess the effect of violating these assumptions (in addition to the effect of noise levels), based on the performances of the univariate monitoring methods, provide an overview of the univariate wavelet-based technique. And then discuss and illustrate the wavelet-based multivariate extension of LVR methods. At the end of the chapter, the methods are demonstrated on distillation column data.
dc.publisherElsevier BV
dc.relation.ispartofDOI:10.1016/c2018-0-05141-5
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/B9780128193655000115
dc.rightsArchived with thanks to Elsevier
dc.titleMultiscale latent variable regression-based process monitoring 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.pages155-191
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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