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    Improved detection of incipient anomalies via multivariate memory monitoring charts: Application to an air flow heating system

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    Type
    Article
    Authors
    Harrou, Fouzi cc
    Madakyaru, Muddu
    Sun, Ying cc
    Khadraoui, Sofiane
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2016-08-11
    Online Publication Date
    2016-08-11
    Print Publication Date
    2016-10
    Permanent link to this record
    http://hdl.handle.net/10754/621495
    
    Metadata
    Show full item record
    Abstract
    Detecting anomalies is important for reliable operation of several engineering systems. Multivariate statistical monitoring charts are an efficient tool for checking the quality of a process by identifying abnormalities. Principal component analysis (PCA) was shown effective in monitoring processes with highly correlated data. Traditional PCA-based methods, nevertheless, often are relatively inefficient at detecting incipient anomalies. Here, we propose a statistical approach that exploits the advantages of PCA and those of multivariate memory monitoring schemes, like the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes to better detect incipient anomalies. Memory monitoring charts are sensitive to incipient anomalies in process mean, which significantly improve the performance of PCA method and enlarge its profitability, and to utilize these improvements in various applications. The performance of PCA-based MEWMA and MCUSUM control techniques are demonstrated and compared with traditional PCA-based monitoring methods. Using practical data gathered from a heating air-flow system, we demonstrate the greater sensitivity and efficiency of the developed method over the traditional PCA-based methods. Results indicate that the proposed techniques have potential for detecting incipient anomalies in multivariate data. © 2016 Elsevier Ltd
    Citation
    Harrou F, Madakyaru M, Sun Y, Khadraoui S (2016) Improved detection of incipient anomalies via multivariate memory monitoring charts: Application to an air flow heating system. Applied Thermal Engineering 109: 65–74. Available: http://dx.doi.org/10.1016/j.applthermaleng.2016.08.047.
    Sponsors
    King Abdullah University of Science and Technology
    Office of Sponsored Research[OSR-2015-CRG4-2582]
    Publisher
    Elsevier BV
    Journal
    Applied Thermal Engineering
    DOI
    10.1016/j.applthermaleng.2016.08.047
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.applthermaleng.2016.08.047
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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