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

Abstract

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 and parameter information been directly extracted, and then a method based on Mathematical morphology is also presented. Mathematical morphology filters 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%.