Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA)

Yichuan Fu; Yuanhong Liu; Aihua Zhang; Zhiwei Gao

doi:10.1109/IECON.2019.8927206

Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA)

Yichuan Fu, Yuanhong Liu, Aihua Zhang, Zhiwei Gao

Mathematics, Physics and Electrical Engineering

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

2 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) system and smart meters. It is challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven fault diagnosis and classification algorithm is addressed by integrating fast Fourier transform (FFT) and multi-linear principal component analysis (MPCA) in order to enhance the capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to a 4.8-MW wind turbine benchmark system, where multiple actuator faults are taken into accounts. The effectiveness of the algorithm is demonstrated by intensive simulations and comparison studies.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	3761-3766
Number of pages	6
ISBN (Electronic)	9781728148786
DOIs	https://doi.org/10.1109/IECON.2019.8927206
Publication status	Published - Oct 2019
Event	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal Duration: 14 Oct 2019 → 17 Oct 2019

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2019-October

Conference

Conference	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/Territory	Portugal
City	Lisbon
Period	14/10/19 → 17/10/19

Keywords

Actuator faults
fast Fourier transform (FFT)
fault classification
multi-linear principal component analysis (M-PCA)
wind turbines

UN SDGs

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

Access to Document

10.1109/IECON.2019.8927206

Cite this

Fu, Y., Liu, Y., Zhang, A., & Gao, Z. (2019). Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA). In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 3761-3766). Article 8927206 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE. https://doi.org/10.1109/IECON.2019.8927206

Fu, Yichuan ; Liu, Yuanhong ; Zhang, Aihua et al. / Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA). Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway, NJ : IEEE, 2019. pp. 3761-3766 (IECON Proceedings (Industrial Electronics Conference)).

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title = "Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA)",

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) system and smart meters. It is challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven fault diagnosis and classification algorithm is addressed by integrating fast Fourier transform (FFT) and multi-linear principal component analysis (MPCA) in order to enhance the capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to a 4.8-MW wind turbine benchmark system, where multiple actuator faults are taken into accounts. The effectiveness of the algorithm is demonstrated by intensive simulations and comparison studies.",

keywords = "Actuator faults, fast Fourier transform (FFT), fault classification, multi-linear principal component analysis (M-PCA), wind turbines",

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

note = "Funding Information: The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under grant 61673074, and the E&E faculty at University of Northumbria (UK).; 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 ; Conference date: 14-10-2019 Through 17-10-2019",

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doi = "10.1109/IECON.2019.8927206",

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Fu, Y, Liu, Y, Zhang, A & Gao, Z 2019, Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA). in Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society., 8927206, IECON Proceedings (Industrial Electronics Conference), vol. 2019-October, IEEE, Piscataway, NJ, pp. 3761-3766, 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019, Lisbon, Portugal, 14/10/19. https://doi.org/10.1109/IECON.2019.8927206

Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA). / Fu, Yichuan; Liu, Yuanhong; Zhang, Aihua et al.
Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway, NJ: IEEE, 2019. p. 3761-3766 8927206 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October).

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

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AU - Gao, Zhiwei

N1 - Funding Information: The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under grant 61673074, and the E&E faculty at University of Northumbria (UK).

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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) system and smart meters. It is challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven fault diagnosis and classification algorithm is addressed by integrating fast Fourier transform (FFT) and multi-linear principal component analysis (MPCA) in order to enhance the capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to a 4.8-MW wind turbine benchmark system, where multiple actuator faults are taken into accounts. The effectiveness of the algorithm is demonstrated by 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) system and smart meters. It is challenging to diagnose and classify multiple faults occurring simultaneously in a system monitored. In this study, a data-driven fault diagnosis and classification algorithm is addressed by integrating fast Fourier transform (FFT) and multi-linear principal component analysis (MPCA) in order to enhance the capability of fault diagnosis and classification for systems subjected to multiple faults. The algorithm proposed is applied to a 4.8-MW wind turbine benchmark system, where multiple actuator faults are taken into accounts. The effectiveness of the algorithm is demonstrated by intensive simulations and comparison studies.

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Fu Y, Liu Y, Zhang A, Gao Z. Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA). In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway, NJ: IEEE. 2019. p. 3761-3766. 8927206. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON.2019.8927206

Multiple Actuator Fault Classification for Wind Turbine Systems by Integrating Fast Fourier Transform (FFT) and Multi-linear Principal Component Analysis (MPCA)

Abstract

Publication series

Conference

Keywords

UN SDGs

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