Prediction and Application of DBN-PSO Vibration in Hot Rolling Mill of Structural Steel Plate
Real-Time Monitoring Data(RMD)was used to analyze mill vibration state.The Deep Belief Networks(DBN)and Particle Swarm Optimization(PSO)algorithms are used to construct a simulation model of rolling mill vibration to realize the Deep mining of RMD parameters and achieve the prediction effect of rolling mill vibration.Through fusion processing,the predic-tion data which is very close to the actual vibration process can be obtained,and it has excellent prediction ability.The prediction error of the initial data combined with the field test is within 3.5%,which is consistent with the rolling mill vibration.When the rolling rate slows down,the vibration acceleration decreases.The inlet tension has a reverse effect on the vibration acceleration of the rolling mill.The vibration acceleration of the rolling mill is positively correlated with the outlet tension.It is found that the vi-bration acceleration of the rolling mill is basically constant when the rolling parts of different widths are tested.The research has a good guiding significance for improving the running stability of hot rolling mill and ensuring the forming accuracy of building structural steel plate,and can be extended to other forming equipment optimization fields.
NETL
NSTL
万方数据
