基于料斗秤的定量装车系统算法

Algorithm for quantitative loading system based on Hopper scale

料斗秤是定量装车系统中的控制核心,其称量精度严重受物料装载冲击噪声的影响,且出现单个传感器故障时严重影响系统的运行。针对上述情况,设计了小波变换和径向基神经网络结合的算法,对称量信号进行有效去噪,并给出有效值替代故障传感器信号保障系统的正常称量,用Matlab仿真,对实际数据进行处理,验证了算法的可行性。经实际应用不仅提高了系统的称量精度,而且提高了系统的可靠性。

Hopper scales is the control core in quantitative loading system,the material loading shock noise seriously affect weighing precision,and emergence of failure of a single sensor seriously affects running of the system. In view of the above situation,design an algorithm combining wavelet transform with radial basis neural network,effectively denoise weighing signals and use valid estimates replace the signal of failed sensor to guarantee normal weighing of system. And process the real data using Matlab simulation,the results show that this algorithm is effective. Through practical application,not only improves weighing precision of system,but also improves reliability of the system.