【摘要】 为实现带式输送机传动滚筒温度的实时监测,利用径向基(RBF)神经网络模型的学习能力,对红外传感器测量值进行修正,降低硬件电路和井下环境对真实值的影响,实现传动滚筒的无接触测温。实验证明:修正后的系统误差保持在1.5℃以内,满足带式输送机在井下的测温要求,具有很好的鲁棒性和泛化力。更多还原

【Abstract】 In order to achieve real-time monitoring of temperature of belt aerotransport roller,this paper used the learning capabilities of radial basis function(RBF) neural network to modify the value of infrared thermal sensor,and minimized the impact of hardware and underground environment.After that the system can non-contacted measuring the temperature of belt aerotransport roller.The experiment proves that the revised system error keeps within 1.5 ℃,it meets the requirement of the belt aerotransport roller work underground.In addition,the system has commendable robustness and better capability of generalization.更多还原