Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data

Jianing Liu; Bingqing Xv; Hongrui Cao; Fengshou Gu; Siwen Chen; Jinhui Li; Bin Yv

doi:10.1007/978-3-031-49421-5_49

Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data

Jianing Liu, Bingqing Xv, Hongrui Cao, Fengshou Gu, Siwen Chen, Jinhui Li, Bin Yv

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The installed capacity of wind turbines has been continuously increasing over the past two decades, but it is hard to implement existing bearing fault detection methods to new wind farms since the lack of fault data. To detect main bearing faults for wind turbines installed in new wind farms without relying on their SCADA data, this paper proposed an across-wind-farms fault detection method named IIFDA-V based on the domain generalization method Information Induced Feature Decomposition and Augmentation (IIFDA). The proposed IIFDA-V optimizes the fault decoder additionally by minimizing the risk differences of source domains. Finally, five fault detection tasks are conducted with 8 operational 2 MW wind turbines in 4 different real wind farms, the results indicate the superiority of the proposed IIFDA-V.

Original language	English
Title of host publication	Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
Editors	Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
Publisher	Springer, Cham
Pages	605-614
Number of pages	10
Volume	152
ISBN (Electronic)	9783031494215
ISBN (Print)	9783031494208, 9783031494239
DOIs	https://doi.org/10.1007/978-3-031-49421-5_49
Publication status	Published - 29 May 2024
Event	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sept 2023 https://unified2023.org/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	152 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated title	UNIfied 2023
Country/Territory	United Kingdom
City	Huddersfield
Period	29/08/23 → 1/09/23
Internet address	https://unified2023.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-031-49421-5_49Licence: Unspecified

Cite this

Liu, J., Xv, B., Cao, H., Gu, F., Chen, S., Li, J., & Yv, B. (2024). Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data. In A. D. Ball, H. Ouyang, J. K. Sinha, & Z. Wang (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2 (Vol. 152, pp. 605-614). (Mechanisms and Machine Science; Vol. 152 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-49421-5_49

Liu, Jianing ; Xv, Bingqing ; Cao, Hongrui et al. / Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data. Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. editor / Andrew D. Ball ; Huajiang Ouyang ; Jyoti K. Sinha ; Zuolu Wang. Vol. 152 Springer, Cham, 2024. pp. 605-614 (Mechanisms and Machine Science).

@inproceedings{dcef8b2f57d94c92b3414626cdf88e1b,

title = "Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data",

abstract = "The installed capacity of wind turbines has been continuously increasing over the past two decades, but it is hard to implement existing bearing fault detection methods to new wind farms since the lack of fault data. To detect main bearing faults for wind turbines installed in new wind farms without relying on their SCADA data, this paper proposed an across-wind-farms fault detection method named IIFDA-V based on the domain generalization method Information Induced Feature Decomposition and Augmentation (IIFDA). The proposed IIFDA-V optimizes the fault decoder additionally by minimizing the risk differences of source domains. Finally, five fault detection tasks are conducted with 8 operational 2 MW wind turbines in 4 different real wind farms, the results indicate the superiority of the proposed IIFDA-V.",

keywords = "Domain generalization, Fault detection, Missing data, SCADA data, Wind turbine main bearing",

author = "Jianing Liu and Bingqing Xv and Hongrui Cao and Fengshou Gu and Siwen Chen and Jinhui Li and Bin Yv",

note = "Funding Information: This work was supported by the National Key R\&D Program of China (Grant No. 2020YFB2007700). Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, UNIfied 2023 ; Conference date: 29-08-2023 Through 01-09-2023",

year = "2024",

month = may,

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language = "English",

isbn = "9783031494208",

volume = "152",

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pages = "605--614",

editor = "Ball, \{Andrew D.\} and Huajiang Ouyang and Sinha, \{Jyoti K.\} and Zuolu Wang",

booktitle = "Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2",

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}

Liu, J, Xv, B, Cao, H, Gu, F, Chen, S, Li, J & Yv, B 2024, Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data. in AD Ball, H Ouyang, JK Sinha & Z Wang (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. vol. 152, Mechanisms and Machine Science, vol. 152 MMS, Springer, Cham, pp. 605-614, The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, Huddersfield, United Kingdom, 29/08/23. https://doi.org/10.1007/978-3-031-49421-5_49

Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data. / Liu, Jianing; Xv, Bingqing; Cao, Hongrui et al.
Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. ed. / Andrew D. Ball; Huajiang Ouyang; Jyoti K. Sinha; Zuolu Wang. Vol. 152 Springer, Cham, 2024. p. 605-614 (Mechanisms and Machine Science; Vol. 152 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data

AU - Liu, Jianing

AU - Xv, Bingqing

AU - Cao, Hongrui

AU - Gu, Fengshou

AU - Chen, Siwen

AU - Li, Jinhui

AU - Yv, Bin

N1 - Funding Information: This work was supported by the National Key R&D Program of China (Grant No. 2020YFB2007700). Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/5/29

Y1 - 2024/5/29

N2 - The installed capacity of wind turbines has been continuously increasing over the past two decades, but it is hard to implement existing bearing fault detection methods to new wind farms since the lack of fault data. To detect main bearing faults for wind turbines installed in new wind farms without relying on their SCADA data, this paper proposed an across-wind-farms fault detection method named IIFDA-V based on the domain generalization method Information Induced Feature Decomposition and Augmentation (IIFDA). The proposed IIFDA-V optimizes the fault decoder additionally by minimizing the risk differences of source domains. Finally, five fault detection tasks are conducted with 8 operational 2 MW wind turbines in 4 different real wind farms, the results indicate the superiority of the proposed IIFDA-V.

AB - The installed capacity of wind turbines has been continuously increasing over the past two decades, but it is hard to implement existing bearing fault detection methods to new wind farms since the lack of fault data. To detect main bearing faults for wind turbines installed in new wind farms without relying on their SCADA data, this paper proposed an across-wind-farms fault detection method named IIFDA-V based on the domain generalization method Information Induced Feature Decomposition and Augmentation (IIFDA). The proposed IIFDA-V optimizes the fault decoder additionally by minimizing the risk differences of source domains. Finally, five fault detection tasks are conducted with 8 operational 2 MW wind turbines in 4 different real wind farms, the results indicate the superiority of the proposed IIFDA-V.

KW - Domain generalization

KW - Fault detection

KW - Missing data

KW - SCADA data

KW - Wind turbine main bearing

UR - https://www.scopus.com/pages/publications/85195592446

UR - https://doi.org/10.1007/978-3-031-49421-5

U2 - 10.1007/978-3-031-49421-5_49

DO - 10.1007/978-3-031-49421-5_49

M3 - Conference contribution

AN - SCOPUS:85195592446

SN - 9783031494208

SN - 9783031494239

VL - 152

T3 - Mechanisms and Machine Science

SP - 605

EP - 614

BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2

A2 - Ball, Andrew D.

A2 - Ouyang, Huajiang

A2 - Sinha, Jyoti K.

A2 - Wang, Zuolu

PB - Springer, Cham

T2 - The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences

Y2 - 29 August 2023 through 1 September 2023

ER -

Liu J, Xv B, Cao H, Gu F, Chen S, Li J et al. Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data. In Ball AD, Ouyang H, Sinha JK, Wang Z, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. Vol. 152. Springer, Cham. 2024. p. 605-614. (Mechanisms and Machine Science). Epub 2024 May 28. doi: 10.1007/978-3-031-49421-5_49

Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data

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