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A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes. / Montazeri, Allahyar; Ansarizadeh, Mohammad Hossein; Arefi, Mehdi.
In: IFAC-PapersOnLine, Vol. 52, No. 13, 31.12.2019, p. 2354-2359.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Montazeri, A, Ansarizadeh, MH & Arefi, M 2019, 'A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes', IFAC-PapersOnLine, vol. 52, no. 13, pp. 2354-2359. https://doi.org/10.1016/j.ifacol.2019.11.558

APA

Montazeri, A., Ansarizadeh, M. H., & Arefi, M. (2019). A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes. IFAC-PapersOnLine, 52(13), 2354-2359. https://doi.org/10.1016/j.ifacol.2019.11.558

Vancouver

Montazeri A, Ansarizadeh MH, Arefi M. A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes. IFAC-PapersOnLine. 2019 Dec 31;52(13):2354-2359. doi: 10.1016/j.ifacol.2019.11.558

Author

Montazeri, Allahyar ; Ansarizadeh, Mohammad Hossein ; Arefi, Mehdi. / A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes. In: IFAC-PapersOnLine. 2019 ; Vol. 52, No. 13. pp. 2354-2359.

Bibtex

@article{c5e84e9bb668402d9bdb96cf805bea65,
title = "A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes",
abstract = "Monitoring and fault detection of industrial processes is an important area of research in data science, helping effective management of the plant by the remote operator. In this article, a data-driven statistical model of a process is estimated using the principal component analysis (PCA) method and theassociated probability density function. The aim is to use the model to monitor and detect the incurred faults in the industrial plant. The experimental data are collected by finding the suitable subsystems of a Recycle Gas in Ethylene Oxide production process, and a subset of nine variables are extracted for further statistical analysis of the system. The performance of the developed model for monitoring purpose is evaluated by using faulty and close to faulty inputs as the new test data.",
author = "Allahyar Montazeri and Ansarizadeh, {Mohammad Hossein} and Mehdi Arefi",
year = "2019",
month = dec,
day = "31",
doi = "10.1016/j.ifacol.2019.11.558",
language = "English",
volume = "52",
pages = "2354--2359",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "IFAC Secretariat",
number = "13",

}

RIS

TY - JOUR

T1 - A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes

AU - Montazeri, Allahyar

AU - Ansarizadeh, Mohammad Hossein

AU - Arefi, Mehdi

PY - 2019/12/31

Y1 - 2019/12/31

N2 - Monitoring and fault detection of industrial processes is an important area of research in data science, helping effective management of the plant by the remote operator. In this article, a data-driven statistical model of a process is estimated using the principal component analysis (PCA) method and theassociated probability density function. The aim is to use the model to monitor and detect the incurred faults in the industrial plant. The experimental data are collected by finding the suitable subsystems of a Recycle Gas in Ethylene Oxide production process, and a subset of nine variables are extracted for further statistical analysis of the system. The performance of the developed model for monitoring purpose is evaluated by using faulty and close to faulty inputs as the new test data.

AB - Monitoring and fault detection of industrial processes is an important area of research in data science, helping effective management of the plant by the remote operator. In this article, a data-driven statistical model of a process is estimated using the principal component analysis (PCA) method and theassociated probability density function. The aim is to use the model to monitor and detect the incurred faults in the industrial plant. The experimental data are collected by finding the suitable subsystems of a Recycle Gas in Ethylene Oxide production process, and a subset of nine variables are extracted for further statistical analysis of the system. The performance of the developed model for monitoring purpose is evaluated by using faulty and close to faulty inputs as the new test data.

U2 - 10.1016/j.ifacol.2019.11.558

DO - 10.1016/j.ifacol.2019.11.558

M3 - Journal article

VL - 52

SP - 2354

EP - 2359

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 13

ER -