摘要
目前提升机的减速点距离确定以等速段的速度为计算依据,从经济性考虑等速速度几乎不变,因此提升机的减速点距离设定方法没有与实际工况结合。采用负载作为计算提升机减速度的依据,将提升机减速段行程用减速度进行表达,此种确定减速点距离的方式可以适应更多的工况。从安全生产的角度出发,对提升机减速度的确定方法进行了设计,并分析了确定减速度的策略。为了提高预测精度,采用双模型RBF神经网络进行减速点预测,通过离线学习与在线校正相结合的方法提高了模型的预测精度。
At present, the speed of the deceleration point of the hoist is determined based on the velocity of the constant velocity section. From the economical consideration, the constant velocity is almost constant, so the method of setting the deceleration point of the hoist is not combined with the actual working conditions. The load was used as the basis for calculating the deceleration of the hoist, and the hoist deceleration section was expressed by the deceleration. This way of determining the deceleration point distance can adapt to more working conditions. From the perspective of safety production, designed the method of determining the deceleration of the hoist and analyzed the strategy of determining the deceleration. In order to improve the prediction accuracy, used the dual-model RBF neural network for prediction of deceleration point. The combination of off-line learning and online correction improves the prediction accuracy of the model.
作者
袁晓曦
Yuan Xiaoxi(Wuhan Vocational College of Software and Engineering,Wuhan 430012,China)
出处
《煤矿机械》
北大核心
2019年第9期194-196,共3页
Coal Mine Machinery
基金
湖北省教育厅科学技术研究项目（B2014211）
