Abstract：A real-time monitoring method of rolling mill chatter based on dynamic bayesian network and DS (Dempster/Shafer) evidence theory is proposed. In the mentioned method, multiple characteristic parameters in time domain and frequency domain are preselected to represent the different characteristics of rolling mill vibration signals under different working conditions, and the symptom parameters with high sensitivity are selected by using the stability discrimination rate method. Then, the dynamic bayesian network and DS evidence theory real-time monitoring model is used to establish the real-time monitoring system of rolling mill chatter state, and the continuous speed load time slices are constructed. Three continuous speed load time slices are taken as the evidence body of DS evidence theory. At the same time, the trust method of optimizing the basic probability distribution is proposed to solve the conflict between the evidence bodies of DS evidence theory. Finally, the experiment is carried out on the rolling mill experimental platform, and the diagnosis results show that the recognition rate of different states of rolling mill chatter can reach 99.05%.
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2022, Vol. 43 
