Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies

Yichuan Fu; Zhiwei Gao; Aihua Zhang; Xiaoxu Liu

doi:10.1109/INDIN45523.2021.9557362

Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies

Yichuan Fu^*, Zhiwei Gao, Aihua Zhang, Xiaoxu Liu

^*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

Data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and smart meters. It is of interest but challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven and supervised machine learning-based fault diagnosis and classification algorithm is addressed by the combination and consolidation among Hilbert-Huang Transformation (HHT), Multi-Linear Principal Component Analysis (MPCA), and Support Vector Machine (SVM) to enhance the feasibility and capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to the 4.8 MW wind turbine benchmark model, where multiple actuator faults are taken into considerations. The effectiveness of the methodology is demonstrated by using intensive simulations and comparison studies.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021
Publisher	IEEE
ISBN (Electronic)	9781728143958
DOIs	https://doi.org/10.1109/INDIN45523.2021.9557362
Publication status	Published - 2021
Event	19th IEEE International Conference on Industrial Informatics, INDIN 2021 - Mallorca, Spain Duration: 21 Jul 2021 → 23 Jul 2021

Publication series

Name	IEEE International Conference on Industrial Informatics (INDIN)
Volume	2021-July
ISSN (Print)	1935-4576

Conference

Conference	19th IEEE International Conference on Industrial Informatics, INDIN 2021
Country/Territory	Spain
City	Mallorca
Period	21/07/21 → 23/07/21

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Data-driven
Fault classification
Hilbert-Huang transformation
Multi-linear principal component analysis
Support vector machine
Wind turbines

Access to Document

10.1109/INDIN45523.2021.9557362

Cite this

Fu, Y., Gao, Z., Zhang, A., & Liu, X. (2021). Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies. In Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021 (IEEE International Conference on Industrial Informatics (INDIN); Vol. 2021-July). IEEE. https://doi.org/10.1109/INDIN45523.2021.9557362

@inproceedings{b1873bee83da4710919662f2b2737b3e,

title = "Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies",

abstract = "Data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and smart meters. It is of interest but challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven and supervised machine learning-based fault diagnosis and classification algorithm is addressed by the combination and consolidation among Hilbert-Huang Transformation (HHT), Multi-Linear Principal Component Analysis (MPCA), and Support Vector Machine (SVM) to enhance the feasibility and capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to the 4.8 MW wind turbine benchmark model, where multiple actuator faults are taken into considerations. The effectiveness of the methodology is demonstrated by using intensive simulations and comparison studies.",

keywords = "Data-driven, Fault classification, Hilbert-Huang transformation, Multi-linear principal component analysis, Support vector machine, Wind turbines",

author = "Yichuan Fu and Zhiwei Gao and Aihua Zhang and Xiaoxu Liu",

note = "Funding Information: The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under Grant 61673074, Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515110234), Shenzhen Science and Technology Program (Grant No. RCBS20200714114921371), and the E\&E faculty at Northumbria University, Newcastle upon Tyne, Tyne and Wear, NE1 8ST, United Kingdom.; 19th IEEE International Conference on Industrial Informatics, INDIN 2021 ; Conference date: 21-07-2021 Through 23-07-2021",

year = "2021",

doi = "10.1109/INDIN45523.2021.9557362",

language = "English",

series = "IEEE International Conference on Industrial Informatics (INDIN)",

publisher = "IEEE",

booktitle = "Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021",

address = "United States",

}

Fu, Y, Gao, Z, Zhang, A & Liu, X 2021, Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies. in Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021. IEEE International Conference on Industrial Informatics (INDIN), vol. 2021-July, IEEE, 19th IEEE International Conference on Industrial Informatics, INDIN 2021, Mallorca, Spain, 21/07/21. https://doi.org/10.1109/INDIN45523.2021.9557362

Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies. / Fu, Yichuan; Gao, Zhiwei; Zhang, Aihua et al.
Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021. IEEE, 2021. (IEEE International Conference on Industrial Informatics (INDIN); Vol. 2021-July).

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

TY - GEN

T1 - Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies

AU - Fu, Yichuan

AU - Gao, Zhiwei

AU - Zhang, Aihua

AU - Liu, Xiaoxu

N1 - Funding Information: The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under Grant 61673074, Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515110234), Shenzhen Science and Technology Program (Grant No. RCBS20200714114921371), and the E&E faculty at Northumbria University, Newcastle upon Tyne, Tyne and Wear, NE1 8ST, United Kingdom.

PY - 2021

Y1 - 2021

N2 - Data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and smart meters. It is of interest but challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven and supervised machine learning-based fault diagnosis and classification algorithm is addressed by the combination and consolidation among Hilbert-Huang Transformation (HHT), Multi-Linear Principal Component Analysis (MPCA), and Support Vector Machine (SVM) to enhance the feasibility and capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to the 4.8 MW wind turbine benchmark model, where multiple actuator faults are taken into considerations. The effectiveness of the methodology is demonstrated by using intensive simulations and comparison studies.

AB - Data-driven fault diagnosis and classification for wind turbine systems have received much attention due to a large amount of data available recorded by supervisory control and data acquisition (SCADA) systems and smart meters. It is of interest but challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven and supervised machine learning-based fault diagnosis and classification algorithm is addressed by the combination and consolidation among Hilbert-Huang Transformation (HHT), Multi-Linear Principal Component Analysis (MPCA), and Support Vector Machine (SVM) to enhance the feasibility and capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to the 4.8 MW wind turbine benchmark model, where multiple actuator faults are taken into considerations. The effectiveness of the methodology is demonstrated by using intensive simulations and comparison studies.

KW - Data-driven

KW - Fault classification

KW - Hilbert-Huang transformation

KW - Multi-linear principal component analysis

KW - Support vector machine

KW - Wind turbines

UR - https://www.scopus.com/pages/publications/85123760902

U2 - 10.1109/INDIN45523.2021.9557362

DO - 10.1109/INDIN45523.2021.9557362

M3 - Conference contribution

AN - SCOPUS:85123760902

T3 - IEEE International Conference on Industrial Informatics (INDIN)

BT - Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021

PB - IEEE

T2 - 19th IEEE International Conference on Industrial Informatics, INDIN 2021

Y2 - 21 July 2021 through 23 July 2021

ER -

Fu Y, Gao Z, Zhang A, Liu X. Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies. In Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021. IEEE. 2021. (IEEE International Conference on Industrial Informatics (INDIN)). doi: 10.1109/INDIN45523.2021.9557362

Fault Classification for Wind Turbine Benchmark Model Based on Hilbert-Huang Transformation and Support Vector Machine Strategies

Abstract

Publication series

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UN SDGs

Keywords

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