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    Case studies

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    Type
    Book Chapter
    Authors
    Harrou, Fouzi cc
    Sun, Ying cc
    Hering, Amanda S.
    Madakyaru, Muddu
    Dairi, Abdelkader
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    Date
    2021
    Permanent link to this record
    http://hdl.handle.net/10754/667740
    
    Metadata
    Show full item record
    Abstract
    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/b978-0-12-819365-5.00014-0
    Publisher
    Elsevier BV
    ISBN
    9780128193655
    DOI
    10.1016/b978-0-12-819365-5.00014-0
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/B9780128193655000140
    Relations
    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
    Scopus Count
    Collections
    Book Chapters; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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