A Fault Diagnosis Method of Rolling Mill Bearing at Low Frequency and Overload Condition Based on Integration of EEMD and GA-DBN

To solve the problems of difficult fault signal recognition and poor diagnosis effect of different damage in the same position in rolling mill bearing at low speed, a fault diagnosis method of rolling mill bearing based on integration of EEMD and DBN was proposed. The vibration signals in horizontal, axial, and vertical directions were decomposed and reconstructed by EEMD, and frequency domain analysis was carried out by using refined spectrum. Then, the signal's time-frequency domain index, rolling force, and torque component feature vector were input into genetic algorithm (GA) to optimize DBN model classification. In order to verify the effectiveness of the method, the experimental study was carried out on the two-high experimental rolling mill. The results show that EEMD combined with thinning spectrum can solve the problem of fault feature extraction well. Compared with time-frequency domain characteristic input, the prediction accuracy of DBN model is obviously improved. And the accuracy of GA-DBN model is higher, and the accuracy is 98.3%, and the time taken to diagnose is significantly reduced. Finally, the fault classification of different parts of bearings and the fault diagnosis of different damage in the same part are realized, which provides a good theoretical basis for the fault diagnosis of low-speed bearings and has important engineering significance.

Automated summary

By integrating EEMD and refined spectrum, the proposed method effectively extracts fault features of rolling mill bearings, improving the prediction accuracy of DBN model compared to time-frequency domain characteristics. The GA-DBN model achieves higher accuracy of 98.3% and significantly reduces diagnosis time, providing a good theoretical basis for fault diagnosis of low-speed bearings.