Research on Bearing Fault Identification of Wind Turbines’ Transmission System Based on Wavelet Packet Decomposition and Probabilistic Neural Network

Li Cao () and Wenlei Sun
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Li Cao: School of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830017, China
Wenlei Sun: School of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830017, China

Energies, 2024, vol. 17, issue 11, 1-16

Abstract: In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate.

Keywords: rolling bearing; EEMD; IMF; the wavelet packet decomposition method; extreme learning machine; the probabilistic neural network; fault identification (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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