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

被引:59
作者
Fu, Yichuan [1 ]
Gao, Zhiwei [1 ]
Liu, Yuanhong [2 ]
Zhang, Aihua [3 ]
Yin, Xiuxia [4 ]
机构
[1] Univ Northumbria, Fac Engn & Environm, Dept Math Phys & Elect Engn, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England
[2] Northeast Petr Univ, Sch Elect Engn & Informat, Daqing 163318, Peoples R China
[3] Bohai Univ, Coll Engn, Jinzhou 121000, Peoples R China
[4] Nanchang Univ, Sch Sci, Dept Math, Nanchang 330000, Jiangxi, Peoples R China
关键词
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; DISCRIMINANT-ANALYSIS; ROBUST-PCA; DIAGNOSIS; NETWORK;
D O I
10.3390/pr8091066
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
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.
引用
收藏
页数:32
相关论文
共 47 条
[1]   Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection [J].
Abid, Anam ;
Khan, Muhammad Tahir ;
Khan, Muhammad Salman .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 50 (01) :348-359
[2]   Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises [J].
Adeli, Ehsan ;
Thung, Kim-Han ;
An, Le ;
Wu, Guorong ;
Shi, Feng ;
Wang, Tao ;
Shen, Dinggang .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2019, 41 (02) :515-522
[3]  
[Anonymous], 2006, Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance
[4]   Machine learning for data-driven discovery in solid Earth geoscience [J].
Bergen, Karianne J. ;
Johnson, Paul A. ;
de Hoop, Maarten V. ;
Beroza, Gregory C. .
SCIENCE, 2019, 363 (6433) :1299-+
[5]  
陈家瑞, 1999, [铁道建筑, Railway Engineering], P19
[6]   Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning [J].
Elforjani, Mohamed ;
Shanbr, Suliman .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 65 (07) :5864-5871
[7]  
Fu YC, 2019, IEEE INTL CONF IND I, P1303, DOI [10.1109/INDIN41052.2019.8972303, 10.1109/indin41052.2019.8972303]
[8]  
Fu YC, 2019, IEEE IND ELEC, P3761, DOI [10.1109/IECON.2019.8927206, 10.1109/iecon.2019.8927206]
[9]   Pitch control for wind turbine systems using optimization, estimation and compensation [J].
Gao, Richie ;
Gao, Zhiwei .
RENEWABLE ENERGY, 2016, 91 :501-515
[10]   Real-time monitoring, prognosis, and resilient control for wind turbine systems [J].
Gao, Zhiwei ;
Sheng, Shuangwen .
RENEWABLE ENERGY, 2018, 116 :1-4