基于数学形态学与PNN的螺杆泵井故障诊断

针对螺杆泵井泵功图图形信息一直没有得到充分利用，在一定程度上影响诊断技术的推广和利用的问题，研究直接提取泵功图状态参数形状特征信息的方法，提出基于数学形态学的泵功图图形特征提取方法。采用开闭组合的数学形态学算子实现泵功图边缘纹理特征提取，对提取的特征数字化后，使用PNN(概率神经网络)进行故障识别。实际应用证明，识别准确率达到90%以上。

Fault Diagnosis Based on Mathematical morphology and Probabilistic Neural Network for Progressing Cavity Pump Well

The problem of a scarce consideration of screw pump well pump diagram graphic information affects the diagnosis technology promotion and utilization to some extent. The method ,through which the shape features in pump diagram graphic state parameters information is directly extracted. , and then a method based on Mathematical morphology is also presented. Mathematical morphology filter of open-close operator to realize graphics edge texture feature extraction. After feature digitized, using a probabilistic neural network to identify fault. The practical application shows the classification accuracy rate is above 90%.