KAUST DepartmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Environmental Statistics Group
Embargo End Date2023-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/667738
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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.
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
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