Fault Propagation Analysis by Implementing Nearest Neighbors Method Using Process Connectivity

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Fault Propagation Analysis by Implementing Nearest Neighbors Method Using Process Connectivity. / Landman, Rinat; Jämsä-Jounela, Sirkka-Liisa.

julkaisussa: IEEE Transactions on Control Systems Technology, Vuosikerta 27, Nro 5, 09.2019, s. 2058-2067.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Bibtex - Lataa

@article{4a0af6c9edfb4a9390a26d0091f7d223,
title = "Fault Propagation Analysis by Implementing Nearest Neighbors Method Using Process Connectivity",
abstract = "Industrial systems often encounter abnormal conditions due to various faults or external disturbances which deteriorate the process performance. In such cases, it is essential to detect and eliminate the root cause of the faulty condition as early as possible in order to minimize its adverse effect on the entire process performance. Capturing the process causality plays a key role in identifying the propagation path of faults and their root cause. In recent times, several data-based methods have been developed in order to capture causality from the measured process data. However, each of the methods suffers from several limitations and deficiencies which might compromise their ability to provide an adequate causal model, especially in multivariate (MV) systems. This paper proposes a new methodology for retracing the propagation path of oscillation using a nearest neighbors method by utilizing the information on process connectivity. The two-phase methodology yields a directionality measure based on the type of connectivity in the process using a unique search algorithm. In phase I, the bivariate directionality measure is calculated to include only the interactions that are considered as direct based on the plant topology. In phase II, a new MV directionality measure based on the nearest neighbors method is introduced in order to exclude indirect interactions. The methodology is successfully demonstrated on industrial board machine exhibiting oscillations in its drying section.",
author = "Rinat Landman and Sirkka-Liisa J{\"a}ms{\"a}-Jounela",
year = "2019",
month = "9",
doi = "10.1109/TCST.2018.2847651",
language = "English",
volume = "27",
pages = "2058--2067",
journal = "IEEE Transactions on Control Systems Technology",
issn = "1063-6536",
publisher = "IEEE",
number = "5",

}

RIS - Lataa

TY - JOUR

T1 - Fault Propagation Analysis by Implementing Nearest Neighbors Method Using Process Connectivity

AU - Landman, Rinat

AU - Jämsä-Jounela, Sirkka-Liisa

PY - 2019/9

Y1 - 2019/9

N2 - Industrial systems often encounter abnormal conditions due to various faults or external disturbances which deteriorate the process performance. In such cases, it is essential to detect and eliminate the root cause of the faulty condition as early as possible in order to minimize its adverse effect on the entire process performance. Capturing the process causality plays a key role in identifying the propagation path of faults and their root cause. In recent times, several data-based methods have been developed in order to capture causality from the measured process data. However, each of the methods suffers from several limitations and deficiencies which might compromise their ability to provide an adequate causal model, especially in multivariate (MV) systems. This paper proposes a new methodology for retracing the propagation path of oscillation using a nearest neighbors method by utilizing the information on process connectivity. The two-phase methodology yields a directionality measure based on the type of connectivity in the process using a unique search algorithm. In phase I, the bivariate directionality measure is calculated to include only the interactions that are considered as direct based on the plant topology. In phase II, a new MV directionality measure based on the nearest neighbors method is introduced in order to exclude indirect interactions. The methodology is successfully demonstrated on industrial board machine exhibiting oscillations in its drying section.

AB - Industrial systems often encounter abnormal conditions due to various faults or external disturbances which deteriorate the process performance. In such cases, it is essential to detect and eliminate the root cause of the faulty condition as early as possible in order to minimize its adverse effect on the entire process performance. Capturing the process causality plays a key role in identifying the propagation path of faults and their root cause. In recent times, several data-based methods have been developed in order to capture causality from the measured process data. However, each of the methods suffers from several limitations and deficiencies which might compromise their ability to provide an adequate causal model, especially in multivariate (MV) systems. This paper proposes a new methodology for retracing the propagation path of oscillation using a nearest neighbors method by utilizing the information on process connectivity. The two-phase methodology yields a directionality measure based on the type of connectivity in the process using a unique search algorithm. In phase I, the bivariate directionality measure is calculated to include only the interactions that are considered as direct based on the plant topology. In phase II, a new MV directionality measure based on the nearest neighbors method is introduced in order to exclude indirect interactions. The methodology is successfully demonstrated on industrial board machine exhibiting oscillations in its drying section.

U2 - 10.1109/TCST.2018.2847651

DO - 10.1109/TCST.2018.2847651

M3 - Article

VL - 27

SP - 2058

EP - 2067

JO - IEEE Transactions on Control Systems Technology

JF - IEEE Transactions on Control Systems Technology

SN - 1063-6536

IS - 5

ER -

ID: 26745698