Bearing Fault Classifications Based on Nerve Network and High Order Statistics
Li Li · Bearing Jurnal Penelitian dan Kajian Teknik Sipil · 2006
The fault classification method for the rolling bearings is put forwards,based on combination of BP nervous networks with higher-order statistics.Taking the higher order statistics(such as bispectrum and three order cumulant)and some common dimensionless index as input values of bearing fault characteristics,the BP nervous networks as segregator,the classification of four different faults is successfully completed.The classification effect is better by comparison with RBF nervous networks,though the training speed of BP nervous networks is not so fast.The results show that it is effective to use the combination of higher-order statistics with BP nervous networks for classification of rolling bearing faults.