Data and Reliability Characterization Strategy for Automatic Detection of Valve Stiction in Control Loops

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Data and Reliability Characterization Strategy for Automatic Detection of Valve Stiction in Control Loops. / Pozo Garcia, Octavio; Zakharov, Alexey; Jamsa-Jounela, Sirkka Liisa.

julkaisussa: IEEE Transactions on Control Systems Technology, Vuosikerta 25, Nro 3, 7505905, 01.05.2017, s. 769-780.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Bibtex - Lataa

@article{7f363df33050421abc23255b21be21e1,
title = "Data and Reliability Characterization Strategy for Automatic Detection of Valve Stiction in Control Loops",
abstract = "Automatic detection of process faults requires process expertise to determine the fault symptoms and mathematical algorithms that can classify these symptoms correctly. Thus, the effectiveness of any automatic detection system can degrade because of inconsistencies in the process data and variations in the assumed symptoms of the fault. This paper presents a novel detection strategy based on the data characterization and reliability analysis. In more detail, at first process data are profiled to automatically select and apply the most suitable detection algorithms. Then, the reliability of the detection decisions made by the selected detection algorithms is evaluated, considering a decision unreliable when the process data fit neither the healthy case nor the faulty case assumed by the method. The strategy is applied to a valve stiction detection system. In addition, an exponential fitting method, recognizing three oscillation patterns associated with stiction, is proposed and incorporated into the system. The resulting system was tested on a benchmark data, and the results are discussed.",
keywords = "Control loops, data characterization, fault detection, oscillation, static friction (stiction), valves",
author = "{Pozo Garcia}, Octavio and Alexey Zakharov and Jamsa-Jounela, {Sirkka Liisa}",
year = "2017",
month = "5",
day = "1",
doi = "10.1109/TCST.2016.2583959",
language = "English",
volume = "25",
pages = "769--780",
journal = "IEEE Transactions on Control Systems Technology",
issn = "1063-6536",
publisher = "IEEE",
number = "3",

}

RIS - Lataa

TY - JOUR

T1 - Data and Reliability Characterization Strategy for Automatic Detection of Valve Stiction in Control Loops

AU - Pozo Garcia, Octavio

AU - Zakharov, Alexey

AU - Jamsa-Jounela, Sirkka Liisa

PY - 2017/5/1

Y1 - 2017/5/1

N2 - Automatic detection of process faults requires process expertise to determine the fault symptoms and mathematical algorithms that can classify these symptoms correctly. Thus, the effectiveness of any automatic detection system can degrade because of inconsistencies in the process data and variations in the assumed symptoms of the fault. This paper presents a novel detection strategy based on the data characterization and reliability analysis. In more detail, at first process data are profiled to automatically select and apply the most suitable detection algorithms. Then, the reliability of the detection decisions made by the selected detection algorithms is evaluated, considering a decision unreliable when the process data fit neither the healthy case nor the faulty case assumed by the method. The strategy is applied to a valve stiction detection system. In addition, an exponential fitting method, recognizing three oscillation patterns associated with stiction, is proposed and incorporated into the system. The resulting system was tested on a benchmark data, and the results are discussed.

AB - Automatic detection of process faults requires process expertise to determine the fault symptoms and mathematical algorithms that can classify these symptoms correctly. Thus, the effectiveness of any automatic detection system can degrade because of inconsistencies in the process data and variations in the assumed symptoms of the fault. This paper presents a novel detection strategy based on the data characterization and reliability analysis. In more detail, at first process data are profiled to automatically select and apply the most suitable detection algorithms. Then, the reliability of the detection decisions made by the selected detection algorithms is evaluated, considering a decision unreliable when the process data fit neither the healthy case nor the faulty case assumed by the method. The strategy is applied to a valve stiction detection system. In addition, an exponential fitting method, recognizing three oscillation patterns associated with stiction, is proposed and incorporated into the system. The resulting system was tested on a benchmark data, and the results are discussed.

KW - Control loops

KW - data characterization

KW - fault detection

KW - oscillation

KW - static friction (stiction)

KW - valves

UR - http://www.scopus.com/inward/record.url?scp=84978952348&partnerID=8YFLogxK

U2 - 10.1109/TCST.2016.2583959

DO - 10.1109/TCST.2016.2583959

M3 - Article

AN - SCOPUS:84978952348

VL - 25

SP - 769

EP - 780

JO - IEEE Transactions on Control Systems Technology

JF - IEEE Transactions on Control Systems Technology

SN - 1063-6536

IS - 3

M1 - 7505905

ER -

ID: 15240848