Articles producció científica> Enginyeria Química

Fault detection and diagnosis in water resource recovery facilities using incremental PCA

  • Datos identificativos

    Identificador: imarina:9139037
    Autores:
    Kazemi, PezhmanGiralt, JaumeBengoa, ChristopheMasoumian, ArminSteyer, Jean-Philippe
    Resumen:
    © IWA Publishing 2020. Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.
  • Otros:

    Autor según el artículo: Kazemi, Pezhman; Giralt, Jaume; Bengoa, Christophe; Masoumian, Armin; Steyer, Jean-Philippe
    Departamento: Enginyeria Química
    Autor/es de la URV: Bengoa, Christophe José / Giralt Marcé, Jaume / Kazemi, Pezhman / Masoumian, Armin
    Palabras clave: Water resources Time-varying processes Principal component analysis Incremental pca Fault isolation Fault detection Eblup Computer simulation Bsm2 Algorithms
    Resumen: © IWA Publishing 2020. Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.
    Áreas temáticas: Zootecnia / recursos pesqueiros Water science and technology Water resources Saúde coletiva Química Planejamento urbano e regional / demografia Odontología Medicina veterinaria Medicina ii Materiais Matemática / probabilidade e estatística Interdisciplinar Geografía Geociências General medicine Farmacia Environmental sciences Environmental engineering Engineering, environmental Engineering, civil Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Economia Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Biotecnología Biodiversidade Administração pública e de empresas, ciências contábeis e turismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 0273-1223
    Direcció de correo del autor: jaume.giralt@urv.cat armin.masoumian@estudiants.urv.cat armin.masoumian@estudiants.urv.cat christophe.bengoa@urv.cat
    Identificador del autor: 0000-0001-5917-8741 0000-0001-9160-5010
    Fecha de alta del registro: 2024-08-10
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://iwaponline.com/wst/article/82/12/2711/75837/Fault-detection-and-diagnosis-in-water-resource
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Water Science And Technology. 82 (12): 2711-2724
    Referencia de l'ítem segons les normes APA: Kazemi, Pezhman; Giralt, Jaume; Bengoa, Christophe; Masoumian, Armin; Steyer, Jean-Philippe (2020). Fault detection and diagnosis in water resource recovery facilities using incremental PCA. Water Science And Technology, 82(12), 2711-2724. DOI: 10.2166/wst.2020.368
    DOI del artículo: 10.2166/wst.2020.368
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Engineering, Civil,Engineering, Environmental,Environmental Engineering,Environmental Sciences,Water Resources,Water Science and Technology
    Water resources
    Time-varying processes
    Principal component analysis
    Incremental pca
    Fault isolation
    Fault detection
    Eblup
    Computer simulation
    Bsm2
    Algorithms
    Zootecnia / recursos pesqueiros
    Water science and technology
    Water resources
    Saúde coletiva
    Química
    Planejamento urbano e regional / demografia
    Odontología
    Medicina veterinaria
    Medicina ii
    Materiais
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geografía
    Geociências
    General medicine
    Farmacia
    Environmental sciences
    Environmental engineering
    Engineering, environmental
    Engineering, civil
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Enfermagem
    Economia
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Biotecnología
    Biodiversidade
    Administração pública e de empresas, ciências contábeis e turismo
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