矿用设备开停传感器自学习工作方法

Self-learning working method of start-stop sensor for mining equipment

目前大部分矿用设备开停传感器检测阈值固定,需人工手动调整开、停状态判定门限,调整过程复杂、不智能。针对这一情况,提出了一种开停传感器自学习工作方法,方法基于现场实际检测的磁场强度,根据统计学原理进行自学习,获得设备开、停判断门限。实践表明,该方法与现有手动调整电位器或遥控器调整寄存器的方法相比,准确度、灵活性、智能化程序等有了大幅提高,更易于现场使用。

At present,the detection thresholds of most on-and off-sensors of most mining equipment are fixed,and the thresholds for on-and off^state determination need to be adjusted manually.The adjustment process is complicated and unintelligent.Aiming at this situation,a working method of start-stop sensor self^leaming was proposed.The method was based on the magnetic field intensity actually detected on the spot,and performs self-learning based on statistical principles to obtain the equipment start-stop threshold.Practice showed that,compared with the existing methods of manually adjusting the potentiometer or the remote controller to adjust the register,the method greatly improved accuracy,flexibility,intelligent programs,etc.,and was easier to use on site.