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

School of Mechanical Engineering

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, Zuolu Wang, Huajiang Ouyang, Jyoti K. Sinha
Publisher	Springer Science and Business Media B.V.
Pages	605-614
Number of pages	10
ISBN (Print)	9783031494208
DOIs	https://doi.org/10.1007/978-3-031-49421-5_49
State	Published - 2024
Event	UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023 - Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sep 2023

Publication series

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

Conference

Conference	UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023
Country/Territory	United Kingdom
City	Huddersfield
Period	29/08/23 → 1/09/23

Keywords

Domain generalization
Fault detection
Missing data
SCADA data
Wind turbine main bearing

Access to Document

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

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, Z. Wang, H. Ouyang, & J. K. Sinha (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2 (pp. 605-614). (Mechanisms and Machine Science; Vol. 152 MMS). Springer Science and Business Media B.V.. 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 ; Zuolu Wang ; Huajiang Ouyang ; Jyoti K. Sinha. Springer Science and Business Media B.V., 2024. pp. 605-614 (Mechanisms and Machine Science).

@inproceedings{fd2075e16f60470d82bfabff1e7117a0,

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 = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023 ; Conference date: 29-08-2023 Through 01-09-2023",

year = "2024",

doi = "10.1007/978-3-031-49421-5\_49",

language = "英语",

isbn = "9783031494208",

series = "Mechanisms and Machine Science",

publisher = "Springer Science and Business Media B.V.",

pages = "605--614",

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

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

}

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, Z Wang, H Ouyang & JK Sinha (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. Mechanisms and Machine Science, vol. 152 MMS, Springer Science and Business Media B.V., pp. 605-614, UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023, 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; Zuolu Wang; Huajiang Ouyang; Jyoti K. Sinha. Springer Science and Business Media B.V., 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 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

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

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

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AN - SCOPUS:85195592446

SN - 9783031494208

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 - Wang, Zuolu

A2 - Ouyang, Huajiang

A2 - Sinha, Jyoti K.

PB - Springer Science and Business Media B.V.

T2 - UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

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, Wang Z, Ouyang H, Sinha JK, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. Springer Science and Business Media B.V. 2024. p. 605-614. (Mechanisms and Machine Science). 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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