Research of Wavelet Neural Network State Degradation Prediction of Rolling Bearing New Time Domain Index - Li Wenfeng; Xu Aiqiang; Sun Jijie; Fan Fuqin (Jixie chuandong)
Aiming at lower accuracy of classification for signal feature extraction of rolling bearing,firstly,some time domain indexes for online simple rapid discrimination are selected. The sensitivity of time domain index of fault is analyzed based on size of bearing fatigue damage and number of local damage. Secondly,based on the traditional time domain index,two more sensitive time domain index ‘TALAF ’and ‘THIKAT ’is searched. Lastly,the data set including two new indicators are trained and tested based on wavelet neural network which has a good real-time. The training and testing results for the traditional time domain indexes kurtosis and BP neural network are compared with results of the data. The simulation results show that TALAF and THIKAT can effectively improve the accuracy of prediction index state bearing.