Autor según el artículo: Kazemi P; Giralt J; Bengoa C; Masoumian A; Steyer JP

Departamento: Enginyeria Química

Autor/es de la URV: Bengoa, Christophe José / KAZEMI, PEZHMAN

Palabras clave: Time-varying processes Incremental pca Fault isolation Fault detection Eblup Bsm2

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: christophe.bengoa@urv.cat

Identificador del autor: 0000-0001-9160-5010

Fecha de alta del registro: 2023-02-19

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: http://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 P; Giralt J; Bengoa C; Masoumian A; Steyer JP (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