基于模糊神经网络的抽油机故障诊断方法

Fault Diagnosis Method of Pumping Unit Based on Fuzzy Neural Network

将模糊逻辑与神经网络相结合,构造模糊神经网络,将神经网络输入层的确定性信息模糊化后变成模糊量,将故障征兆参数相对应的隶属度数值作为神经网络的输入,从而使神经网络更加适合设备故障描述,克服了神经网络对不精确信息表达的缺点。提出基于黄金分割法的变步长BP算法来训练神经网络,根据误差变化趋势动态调整学习速率,实现学习步长的自适应调整,提高网络收敛的速度,防止网络训练时陷入局部极小。将训练好的模糊神经网络应用于抽油机设备的故障诊断,取得良好效果。

Combining with fuzzy logic and neural network,fuzzy neural network is constructed.After the neural network layer of certainty input information turns into fuzzy sense then becomes fuzzy quantity.Take the fault symptom parameters corresponding to the membership values as the input of network,then the neural network is more suitable for equipment fault description,it overcomes the neural network defect of inaccurate information expression.Based on the golden section method of variable step-size BP algorithm to train the neural network.According to the error tendency dynamically adjusting rate,achieves the learning step of adaptive adjustment.It improves the network convergence speed,prevents network training sink into local minimum.The trained fuzzy neural network is applied to fault diagnosis of pumping equipments and achieves good results.