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 - http://www.scopus.com/inward/record.url?scp=85123760902&partnerID=8YFLogxK
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 - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Industrial Informatics, INDIN 2021
Y2 - 21 July 2021 through 23 July 2021
ER -
