Conclusion and further research directions
dc.contributor.author | Harrou, Fouzi | |
dc.contributor.author | Sun, Ying | |
dc.contributor.author | Hering, Amanda S. | |
dc.contributor.author | Madakyaru, Muddu | |
dc.contributor.author | Dairi, Abdelkader | |
dc.date.accessioned | 2021-03-01T06:31:42Z | |
dc.date.available | 2021-03-01T06:31:42Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Harrou, 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.isbn | 9780128193655 | |
dc.identifier.doi | 10.1016/b978-0-12-819365-5.00015-2 | |
dc.identifier.uri | http://hdl.handle.net/10754/667738 | |
dc.description.abstract | Developing 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.publisher | Elsevier BV | |
dc.relation.ispartof | DOI:10.1016/c2018-0-05141-5 | |
dc.relation.url | https://linkinghub.elsevier.com/retrieve/pii/B9780128193655000152 | |
dc.rights | Archived with thanks to Elsevier | |
dc.title | Conclusion and further research directions | |
dc.type | Book Chapter | |
dc.contributor.department | Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division | |
dc.contributor.department | Environmental Statistics Group | |
dc.contributor.department | Statistics Program | |
dc.rights.embargodate | 2023-01-01 | |
dc.eprint.version | Pre-print | |
dc.contributor.institution | Baylor University, Dept of Statistical Science, Waco, TX, United States. | |
dc.contributor.institution | Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. | |
dc.contributor.institution | University of Science and Technology of Oran-Mohamed Boudiaf, Computer Science Department, Signal, Image and Speech Laboratory, Oran, Algeria. | |
dc.identifier.pages | 305-309 | |
kaust.person | Harrou, Fouzi | |
kaust.person | Sun, 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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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
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