Type
Book ChapterKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2021Embargo End Date
2023-01-01Permanent link to this record
http://hdl.handle.net/10754/667738
Metadata
Show full item recordAbstract
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.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/b978-0-12-819365-5.00015-2Publisher
Elsevier BVISBN
9780128193655Additional Links
https://linkinghub.elsevier.com/retrieve/pii/B9780128193655000152Relations
Is Part Of:- [Book]
Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches. (2021). doi:10.1016/c2018-0-05141-5. DOI: 10.1016/c2018-0-05141-5 Handle: 10754/667757
ae974a485f413a2113503eed53cd6c53
10.1016/b978-0-12-819365-5.00015-2