Bihocerence based industrial control loop nonlinearity detection and diagnosis in short nonstationary time series

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Bihocerence based industrial control loop nonlinearity detection and diagnosis in short nonstationary time series. / Lang, Xun; Lu, Shan; Xie, Lei; Zakharov, Alexey; Zhong, Dan; Jämsä-Jounela, Sirkka Liisa.

julkaisussa: Journal of Process Control, Vuosikerta 63, 01.03.2018, s. 15-28.

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Lang, Xun ; Lu, Shan ; Xie, Lei ; Zakharov, Alexey ; Zhong, Dan ; Jämsä-Jounela, Sirkka Liisa. / Bihocerence based industrial control loop nonlinearity detection and diagnosis in short nonstationary time series. Julkaisussa: Journal of Process Control. 2018 ; Vuosikerta 63. Sivut 15-28.

Bibtex - Lataa

@article{8e8b14afd36c4cea9800bc4e40312da5,
title = "Bihocerence based industrial control loop nonlinearity detection and diagnosis in short nonstationary time series",
abstract = "Higher order statistics (HOS) have been widely adopted to diagnose the poor control loop performance in recent years. The existing HOS tools, including bispectrum, bicoherence and bicepstrum, can easily detect severe nonlinearity, but it is still an open problem to ensure the detecting performance when short time series and non-significant nonlinearity are taken into account. In this paper, a new cepstral definition of bicoherence is proposed, namely, bihocerence, which normalizes the traditional bicepstrum. Consequently, a novel statistical index for nonlinearity characterization is defined based on bihocerence. Determination of the correct confidence limit is accomplished by utilizing surrogate data with de-trending and re-trending procedures. Compared with the existing HOS methods, the bihocerence test provides more reliable nonlinearity detection results when dealing with small nonstationary time series and weak nonlinearity. This allows the online application of bihocerence approach to detect process nonlinearity at its early stage. The validity of the raised approach is demonstrated on a series of simulations as well as industrial cases.",
keywords = "Bihocerence, Higher-order statistics, Nonlinearity detection and diagnosis, Surrogate data",
author = "Xun Lang and Shan Lu and Lei Xie and Alexey Zakharov and Dan Zhong and J{\"a}ms{\"a}-Jounela, {Sirkka Liisa}",
year = "2018",
month = "3",
day = "1",
doi = "10.1016/j.jprocont.2018.01.001",
language = "English",
volume = "63",
pages = "15--28",
journal = "Journal of Process Control",
issn = "0959-1524",
publisher = "Elsevier Limited",

}

RIS - Lataa

TY - JOUR

T1 - Bihocerence based industrial control loop nonlinearity detection and diagnosis in short nonstationary time series

AU - Lang, Xun

AU - Lu, Shan

AU - Xie, Lei

AU - Zakharov, Alexey

AU - Zhong, Dan

AU - Jämsä-Jounela, Sirkka Liisa

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Higher order statistics (HOS) have been widely adopted to diagnose the poor control loop performance in recent years. The existing HOS tools, including bispectrum, bicoherence and bicepstrum, can easily detect severe nonlinearity, but it is still an open problem to ensure the detecting performance when short time series and non-significant nonlinearity are taken into account. In this paper, a new cepstral definition of bicoherence is proposed, namely, bihocerence, which normalizes the traditional bicepstrum. Consequently, a novel statistical index for nonlinearity characterization is defined based on bihocerence. Determination of the correct confidence limit is accomplished by utilizing surrogate data with de-trending and re-trending procedures. Compared with the existing HOS methods, the bihocerence test provides more reliable nonlinearity detection results when dealing with small nonstationary time series and weak nonlinearity. This allows the online application of bihocerence approach to detect process nonlinearity at its early stage. The validity of the raised approach is demonstrated on a series of simulations as well as industrial cases.

AB - Higher order statistics (HOS) have been widely adopted to diagnose the poor control loop performance in recent years. The existing HOS tools, including bispectrum, bicoherence and bicepstrum, can easily detect severe nonlinearity, but it is still an open problem to ensure the detecting performance when short time series and non-significant nonlinearity are taken into account. In this paper, a new cepstral definition of bicoherence is proposed, namely, bihocerence, which normalizes the traditional bicepstrum. Consequently, a novel statistical index for nonlinearity characterization is defined based on bihocerence. Determination of the correct confidence limit is accomplished by utilizing surrogate data with de-trending and re-trending procedures. Compared with the existing HOS methods, the bihocerence test provides more reliable nonlinearity detection results when dealing with small nonstationary time series and weak nonlinearity. This allows the online application of bihocerence approach to detect process nonlinearity at its early stage. The validity of the raised approach is demonstrated on a series of simulations as well as industrial cases.

KW - Bihocerence

KW - Higher-order statistics

KW - Nonlinearity detection and diagnosis

KW - Surrogate data

UR - http://www.scopus.com/inward/record.url?scp=85044395910&partnerID=8YFLogxK

U2 - 10.1016/j.jprocont.2018.01.001

DO - 10.1016/j.jprocont.2018.01.001

M3 - Article

AN - SCOPUS:85044395910

VL - 63

SP - 15

EP - 28

JO - Journal of Process Control

JF - Journal of Process Control

SN - 0959-1524

ER -

ID: 18791246