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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.accessioned2021-03-01T06:31:42Z
dc.date.available2021-03-01T06:31:42Z
dc.date.issued2021
dc.identifier.citationHarrou, F., Sun, Y., Hering, A. S., Madakyaru, M., & Dairi, A. (2021). Conclusion and further research directions. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches, 305–309. doi:10.1016/b978-0-12-819365-5.00015-2
dc.identifier.isbn9780128193655
dc.identifier.doi10.1016/b978-0-12-819365-5.00015-2
dc.identifier.urihttp://hdl.handle.net/10754/667738
dc.description.abstractDeveloping efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.
dc.publisherElsevier BV
dc.relation.ispartofDOI:10.1016/c2018-0-05141-5
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/B9780128193655000152
dc.rightsArchived with thanks to Elsevier
dc.titleConclusion and further research directions
dc.typeBook Chapter
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.rights.embargodate2023-01-01
dc.eprint.versionPre-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.pages305-309
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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