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    Fault isolation

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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 Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2021
    Embargo End Date
    2023
    Permanent link to this record
    http://hdl.handle.net/10754/667752
    
    Metadata
    Show full item record
    Abstract
    When multivariate observations are monitored, detection of a fault is merely the first step of the process. Knowledge of the existence of the fault is not particularly informative in high-dimensional settings. Thus, the field of fault isolation has developed to identify those variables that have been affected by the fault. Variables affected by a fault are termed shifted variables, regardless of the nature of the fault. In this chapter, we will first present some pitfalls to be avoided when performing fault isolation and also illustrate the importance of isolating important variables associated with the fault, which can also improve the speed of fault detection. Traditional approaches to removing variables and recalculating monitoring statistics, such as and Q, to isolate the variables will be presented. Next, more modern approaches using variable selection techniques, which typically involve penalized regression, will be described. Both prior approaches work in unsupervised settings where the types of faults that will be observed cannot be anticipated in advance. In settings where a process is very well-studied, a catalogue of data associated with multiple types of faults may exist, so supervised classification methods may be used. We close with some metrics that may be used to assess the performance of fault isolation methods along with two detailed case studies for illustration.
    Citation
    Harrou, F., Sun, Y., Hering, A. S., Madakyaru, M., & Dairi, A. (2021). Fault isolation. Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches, 71–117. doi:10.1016/b978-0-12-819365-5.00009-7
    Publisher
    Elsevier BV
    ISBN
    9780128193655
    DOI
    10.1016/b978-0-12-819365-5.00009-7
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/B9780128193655000097
    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/b978-0-12-819365-5.00009-7
    Scopus Count
    Collections
    Book Chapters; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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