Author, as appears in the article.: Kazemi P; Giralt J; Bengoa C; Masoumian A; Steyer JP

Department: Enginyeria Química

URV's Author/s: Bengoa, Christophe José / KAZEMI, PEZHMAN

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

Abstract: © 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.

Thematic Areas: 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

licence for use: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 0273-1223

Author's mail: christophe.bengoa@urv.cat

Author identifier: 0000-0001-9160-5010

Record's date: 2023-02-19

Papper version: info:eu-repo/semantics/publishedVersion

Link to the original source: https://iwaponline.com/wst/article/82/12/2711/75837/Fault-detection-and-diagnosis-in-water-resource

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Water Science And Technology. 82 (12): 2711-2724

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

Article's DOI: 10.2166/wst.2020.368

Entity: Universitat Rovira i Virgili

Journal publication year: 2020

Publication Type: Journal Publications