Abstract
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.
Highlights
Rolling bearings are widely used and damaged parts in rotary machinery, and their running state directly affects the working performance of the system [1]. e working environment of strip mill is bad. 70% faults of transmission system are related to bearing, and the working state of bearing directly affects the quality of strip products [2]. e development trend of strip rolling mills is high-speed production
Low-frequency features are concentrated on the left end of the spectrum, which is more difficult to identify than high-frequency features, so the frequency analysis of low-frequency bearings is usually more difficult than that of high-frequency bearings
The bearing is subjected to rolling force, so the bearing is in a heavy load state, and the working condition is complex. erefore, it is urgent to solve the problem of fault feature extraction under low-frequency and heavy load condition of rolling bearing of strip mill and low accuracy of damage diagnosis of different positions
Summary
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. E 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. En, 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. Compared with time-frequency domain characteristic input, the prediction accuracy of DBN model is obviously improved. 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
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