信息融合技术在矿井提升机故障诊断中的应用

Application of information fusion technology to fault diagnosis of mine hoist

为了提高矿井提升机故障诊断的准确性,引入了一种基于改进证据理论信息融合的故障诊断方法。首先采用蚁群神经网络、BP网络以及RBF网络分别对提升机进行初步故障诊断,再由改进证据理论进行融合决策。仿真实例表明,该方法可有效解决高冲突证据间的信息融合问题,提高了证据理论的应用能力,并具有一定的容错性,适用于矿井提升机的故障诊断。

To improve the accuracy of the fault diagnosis of the mine hoist, a fault diagnosis method based on improved evidence theory and information fusion technology was designed. First, the ant-colony neural network, BP network and RBF network were respectively used to conduct preliminary fault diagnosis of the mine hoist. And then the fusion decision was made by improved evidence theory. Simulation example showed that the method could effectively solve the fusion of severe conflicting evidences, improved the application ability of the evidence theory, and possessed some fault tolerance capability. The method was suitable for fault diagnosis of the mine hoist.