Open AccessRecognition of Fault State of RV Reducer Based on self-organizing feature map Neural Network
Author(s) -
Zhuanzhe Zhao,
Guowen Ye,
Yongming Liu,
Zhen Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1986/1/012086
Subject(s) - reducer , artificial neural network , fault (geology) , pattern recognition (psychology) , computer science , feature (linguistics) , artificial intelligence , wavelet , self organizing map , probabilistic neural network , identification (biology) , engineering , time delay neural network , linguistics , philosophy , civil engineering , seismology , geology , botany , biology
In order to accurately evaluate the working state of RV reducer, a fault identification method based on the fault identification model established by Self-Organizing Feature Map (SOM) Neural Network is proposed. Firstly, the data measured by the RV reducer test platform are analyzed by wavelet to obtain the wavelet coefficient. Then, combined with the efficiency data of RV reducer, the mean square frequency, center of gravity frequency and frequency variance of the two groups of data are calculated after Fourier transform and power spectrum analysis. After optimization, several eigenvalues are obtained. The eigenvalues are input into the competitive neural network and SOM neural network to establish the fault identification model. Finally, the results of the fault identification model established by the competitive neural network and SOM neural network are compared. The prediction results show that the fault identification model established by SOM neural network can effectively determine the working state of RV reducer.