Type
Book ChapterKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Statistics Program
Date
2021Permanent link to this record
http://hdl.handle.net/10754/667740
Metadata
Show full item recordAbstract
Addressing anomaly detection and attribution is essential to promptly detect abnormalities, and it aids the decision-making of operators, allowing them to better optimize performance, take corrective actions, and maintain downstream processes. Recently, deep learning models have developed rapidly, especially in terms of their learning capabilities. In this chapter, we propose a novel hybrid deep-learning-based anomaly detection method. In particular, we focus on the benefits of deep learning models due to their greedy learning features and the sensitivity of clustering approaches to reveal anomalies in the monitoring process. In this chapter, we discuss and present applications of some deep-learning-based monitoring methods. We apply the developed approaches to monitor many processes, such as detection of obstacles in driving environments for autonomous robots and vehicles, monitoring of wastewater treatment plants, and detection of ozone pollution.Citation
Harrou, F., Sun, Y., Hering, A. S., Madakyaru, M., & Dairi, A. (2021). Case studies. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches, 255–303. doi:10.1016/b978-0-12-819365-5.00014-0Publisher
Elsevier BVISBN
9780128193655Additional Links
https://linkinghub.elsevier.com/retrieve/pii/B9780128193655000140Relations
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/b978-0-12-819365-5.00014-0