基于Elman神经网络矿用通风机故障诊断的研究

Research on Fault Diagnosis System of Mine Ventilator Based on Elman Neural Network

介绍了一种基于Elman神经网络的通风机故障诊断的诊断原理,学习算法以及技术路线。通过对现场信号特征数据的采集以及归一化处理,对Elman神经网络选取最优的结构与参数,实现了煤矿主通风机故障类型的智能分类与诊断。与传统BP神经网络诊断结果相比较,Elman神经网络综合诊断性能更优。最后通过风机的故障诊断实例表明:Elman神经网络在提高学习速度上有了很大的改进,并且有效地抑制了传统神经网络容易陷于局部极小的缺陷,缩短了自主学习的时间,是风机故障诊断的有效方法。

Theory, learning algorithm and technical route of Elman neural are introduced. Though acquainting fault signals on-site and normalizing characteristic data, this method realized intelligent diagnosis of ventilator by constructing optimum structure and parameters based on Elman neural network. Compared with traditional BP neural network, Elman network had better comprehensive performance in diagnosis of ventilator. The result for fault diagnosis of ventilator showed that Elman network improves study speed, represses network to sink local minimum, shortens study time, and Elman neural is effective method for fault diagnosis of ventilator.