基于定子电流特性分析的渣浆泵故障预测

Fault prediction of slurry pump based on multi-scale characteristic analysis of stator current

磨矿分级流程使用渣浆泵输送矿浆并为旋流器提供分级所需的入口压力。渣浆泵输送的矿浆颗粒较大、矿浆浓度与粒度未知,易引发堵转、短路等事故,且事故难以预测。针对这一问题,利用定子电流中所反映的渣浆泵运行信息,结合EMD自适应分解方法与LSTM学习算法,提出了基于定子电流多尺度特性分析的渣浆泵故障预测方法。利用工业数据验证表明,该方法可有效预测矿用渣浆泵故障,可帮助生产技术人员提前给出切换备用泵的决策依据。

Slurry pump in grinding and classification process is used as the equipment to transport pulp and provide inlet pressure required for classification of hydro-cyclone.Particles in slurry transported by slurry pump are too large and the pulp concentration and particle size are unknown,which are easy to cause accidents such as rotor blocking and short circuit,and the accidents are difficult to predict.In order to solve this problem,a fault prediction method of slurry pump based on multi-scale characteristic analysis of stator current is proposed by making full use of the operation information of slurry pump reflected in stator current,combined with EMD adaptive decomposition method and LSTM learning algorithm.The industrial data verification shows that the method can effectively predict the fault of mine slurry pump,and can help the production and technical personnel to give the decision-making basis for switching the standby pump in advance.