Autor segons l'article: Kazemi, Pezhman; Giralt, Jaume; Bengoa, Christophe; Masoumian, Armin; Steyer, Jean-Philippe
Departament: Enginyeria Química
Autor/s de la URV: Bengoa, Christophe José / Giralt Marcé, Jaume / Kazemi, Pezhman / Masoumian, Armin
Paraules clau: Water resources Time-varying processes Principal component analysis Incremental pca Fault isolation Fault detection Eblup Computer simulation Bsm2 Algorithms
Resum: © 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.
Àrees temàtiques: 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
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 0273-1223
Adreça de correu electrònic de l'autor: jaume.giralt@urv.cat armin.masoumian@estudiants.urv.cat armin.masoumian@estudiants.urv.cat christophe.bengoa@urv.cat
Identificador de l'autor: 0000-0001-5917-8741 0000-0001-9160-5010
Data d'alta del registre: 2024-08-10
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://iwaponline.com/wst/article/82/12/2711/75837/Fault-detection-and-diagnosis-in-water-resource
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Water Science And Technology. 82 (12): 2711-2724
Referència 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 de l'article: 10.2166/wst.2020.368
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications
Repositori URV