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    Rights statement: This is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechanical Systems and Signal Processing, 174, 2022 DOI: 10.1016/j.ymssp.2022.109082

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Abnormal vibration detection of wind turbine based on temporal convolution network and multivariate coefficient of variation

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Abnormal vibration detection of wind turbine based on temporal convolution network and multivariate coefficient of variation. / Zhan, Jun ; Wu, Chengkun; Ma, Xiandong; Yang, Canqun ; Miao, Qiucheng ; Wang, Shilin.

In: Mechanical Systems and Signal Processing, Vol. 174, 109082, 15.07.2022.

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Zhan, Jun ; Wu, Chengkun ; Ma, Xiandong ; Yang, Canqun ; Miao, Qiucheng ; Wang, Shilin. / Abnormal vibration detection of wind turbine based on temporal convolution network and multivariate coefficient of variation. In: Mechanical Systems and Signal Processing. 2022 ; Vol. 174.

Bibtex

@article{51c855fe531b46ec981f2d60bd0bcbc3,
title = "Abnormal vibration detection of wind turbine based on temporal convolution network and multivariate coefficient of variation",
abstract = "A working wind turbine generates a large amount of multivariate time-series data, which contain abundant operation state information and can predict impending anomalies. The anomaly detection of the wind turbine nacelle that houses all of the generating components in a turbine have been challenging due to its inherent complexities, systematic oscillations and noise. To address these problems, this paper proposes an unsupervised time-series anomaly detection approach, which combines deep learning with multi-parameter relative variability detection. A normal behavior model (NBM) of nacelle vibration is firstly built upon training normal historical data of the supervisory control and data acquisition (SCADA) system in the high-resolution domain. To better capture the temporal characteristics and frequency information of vibration signals, the vibration spectrum vector is integrated with the multivariate time-series data as inputs and the spectrum-embedded temporal convolutional network (SETCN) is then used to extract latent features. The anomalies are detected through a multi-variate coefficient of variation (MCV) based anomaly assessment index (AAI) of relative variability among vibration residuals and environment parameters of the nacelle. The approach considers the time-series characteristics of input data and preserves the spatio-temporal correlation between variables. Validations using data collected from real-world wind farms demonstrate the effectiveness of the proposed approach.",
keywords = "Abnormal detection, Wind turbine, Supervisory control and data acquisition (SCADA) data, Multivariate coefficient of variation (MCV)",
author = "Jun Zhan and Chengkun Wu and Xiandong Ma and Canqun Yang and Qiucheng Miao and Shilin Wang",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechanical Systems and Signal Processing, 174, 2022 DOI: 10.1016/j.ymssp.2022.109082",
year = "2022",
month = jul,
day = "15",
doi = "10.1016/j.ymssp.2022.109082",
language = "English",
volume = "174",
journal = "Mechanical Systems and Signal Processing",
issn = "0888-3270",
publisher = "Academic Press Inc.",

}

RIS

TY - JOUR

T1 - Abnormal vibration detection of wind turbine based on temporal convolution network and multivariate coefficient of variation

AU - Zhan, Jun

AU - Wu, Chengkun

AU - Ma, Xiandong

AU - Yang, Canqun

AU - Miao, Qiucheng

AU - Wang, Shilin

N1 - This is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechanical Systems and Signal Processing, 174, 2022 DOI: 10.1016/j.ymssp.2022.109082

PY - 2022/7/15

Y1 - 2022/7/15

N2 - A working wind turbine generates a large amount of multivariate time-series data, which contain abundant operation state information and can predict impending anomalies. The anomaly detection of the wind turbine nacelle that houses all of the generating components in a turbine have been challenging due to its inherent complexities, systematic oscillations and noise. To address these problems, this paper proposes an unsupervised time-series anomaly detection approach, which combines deep learning with multi-parameter relative variability detection. A normal behavior model (NBM) of nacelle vibration is firstly built upon training normal historical data of the supervisory control and data acquisition (SCADA) system in the high-resolution domain. To better capture the temporal characteristics and frequency information of vibration signals, the vibration spectrum vector is integrated with the multivariate time-series data as inputs and the spectrum-embedded temporal convolutional network (SETCN) is then used to extract latent features. The anomalies are detected through a multi-variate coefficient of variation (MCV) based anomaly assessment index (AAI) of relative variability among vibration residuals and environment parameters of the nacelle. The approach considers the time-series characteristics of input data and preserves the spatio-temporal correlation between variables. Validations using data collected from real-world wind farms demonstrate the effectiveness of the proposed approach.

AB - A working wind turbine generates a large amount of multivariate time-series data, which contain abundant operation state information and can predict impending anomalies. The anomaly detection of the wind turbine nacelle that houses all of the generating components in a turbine have been challenging due to its inherent complexities, systematic oscillations and noise. To address these problems, this paper proposes an unsupervised time-series anomaly detection approach, which combines deep learning with multi-parameter relative variability detection. A normal behavior model (NBM) of nacelle vibration is firstly built upon training normal historical data of the supervisory control and data acquisition (SCADA) system in the high-resolution domain. To better capture the temporal characteristics and frequency information of vibration signals, the vibration spectrum vector is integrated with the multivariate time-series data as inputs and the spectrum-embedded temporal convolutional network (SETCN) is then used to extract latent features. The anomalies are detected through a multi-variate coefficient of variation (MCV) based anomaly assessment index (AAI) of relative variability among vibration residuals and environment parameters of the nacelle. The approach considers the time-series characteristics of input data and preserves the spatio-temporal correlation between variables. Validations using data collected from real-world wind farms demonstrate the effectiveness of the proposed approach.

KW - Abnormal detection

KW - Wind turbine

KW - Supervisory control and data acquisition (SCADA) data

KW - Multivariate coefficient of variation (MCV)

U2 - 10.1016/j.ymssp.2022.109082

DO - 10.1016/j.ymssp.2022.109082

M3 - Journal article

VL - 174

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

M1 - 109082

ER -