Recognition of Fault State of RV Reducer Based on self-organizing feature map Neural Network

Abstract

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.

Review on vibration test and fault analysis of rv reducer

“…The research results still have a certain guiding role for the current fault diagnosis technology Signal analysis technology has become a necessary condition to effectively extract the real information of mechanical equipment. Commonly used mechanical signal processing methods are: time domain analysis, frequency domain analysis and time-frequency domain analysis [26].…”