Tension force prediction method for mine-used belt conveyor
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摘要: 针对现有带式输送机张紧力检测装置不能根据负载实现张紧力实时预测的问题，提出了一种基于BP神经网络的带式输送机张紧力预测方法。首先分析了带式输送机张紧力、运行阻力与负载之间的关系，探究了负载与带式输送机电动机电流之间的表征关系。然后建立BP神经网络对负载进行预测:根据负载与张紧力的函数关系，获取带式输送机运行过程中的负载变化趋势，由负载实时预测张紧力，根据预测的张紧力对带式输送机进行自适应调控。实验结果表明，该方法的预测精度为92.1%，能够满足带式输送机张紧力实时预测的需求。Abstract: In view of problem that existing tension force detection device of belt conveyor could not realize real-time prediction of tension force according to load, a tension force prediction method based on BP neural network was proposed. Firstly, the relationships among the tension force, running resistance and load of the belt conveyor were analyzed, and the relationship between the load and motor current of the belt conveyor was explored. Then BP neural network was established to predict the load. According to the functional relationship between load and tension force, load variation trend in the running process of the belt conveyor was obtained, and the tension force was real-timely predicted by the load. The belt conveyor was adjusted adaptively according to the predicted tension force. The experimental results show that the prediction accuracy of the method is 92.1%, which can meet demand of real-time prediction of tension force of belt conveyor.
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Keywords:
- mine-used belt conveyor /
- tension force prediction /
- load /
- adaptive control /
- BP neural network
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期刊类型引用(10)
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