Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Lei Fu, Tiantian Zhu, Kai Zhu and Yiling Yang
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Lei Fu: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Tiantian Zhu: College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou 310023, China
Kai Zhu: College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Yiling Yang: Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China

Energies, 2019, vol. 12, issue 16, 1-20

Abstract: Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

Keywords: condition monitoring; wind turbine; variational mode decomposition; fisher score; permutation entropy; variable operational condition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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