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Volume 8
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10.3390/pr8091066
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Article

Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

by
Yichuan Fu
1,
Zhiwei Gao
1,*,
Yuanhong Liu
2,
Aihua Zhang
3 and
Xiuxia Yin
4
1
Department of Mathematics, Physics and Electrical Engineering, Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, UK
2
School of Electrical Engineering and Information, Northeast Petroleum University, Daqing 163318, China
3
College of Engineering, Bohai University, Jinzhou 121000, China
4
Department of Mathematics, School of Science, Nanchang University, Nanchang 330000, China
*
Author to whom correspondence should be addressed.
Processes 2020, 8(9), 1066; https://doi.org/10.3390/pr8091066
Submission received: 4 May 2020 / Revised: 15 August 2020 / Accepted: 25 August 2020 / Published: 1 September 2020
(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)
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Abstract

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark.
Keywords: fault diagnosis; fault classification; fast Fourier transform (FFT); multi-linear principal component analysis (MPCA); uncorrelated multi-linear principal component analysis (UMPCA); additive white Gaussian noises (AWGN); wind turbine systems fault diagnosis; fault classification; fast Fourier transform (FFT); multi-linear principal component analysis (MPCA); uncorrelated multi-linear principal component analysis (UMPCA); additive white Gaussian noises (AWGN); wind turbine systems

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MDPI and ACS Style

Fu, Y.; Gao, Z.; Liu, Y.; Zhang, A.; Yin, X. Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques. Processes 2020, 8, 1066. https://doi.org/10.3390/pr8091066

AMA Style

Fu Y, Gao Z, Liu Y, Zhang A, Yin X. Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques. Processes. 2020; 8(9):1066. https://doi.org/10.3390/pr8091066

Chicago/Turabian Style

Fu, Yichuan, Zhiwei Gao, Yuanhong Liu, Aihua Zhang, and Xiuxia Yin. 2020. "Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques" Processes 8, no. 9: 1066. https://doi.org/10.3390/pr8091066

APA Style

Fu, Y., Gao, Z., Liu, Y., Zhang, A., & Yin, X. (2020). Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques. Processes, 8(9), 1066. https://doi.org/10.3390/pr8091066

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