Identification of Magnetic Flux Leak Inspection Signals for Pipeline Detection Using an Improved BP Neural Netwok Algorithm.

Recognizing the appearance parameters of defects is the main object in the signal processing of offshore oil pipeline inspection. Based on the feature extraction of magnetic flux leakage (MFL) inspection data, a modified back-propagation (BP) neural network system were presented to recognize pipeline defect and corrosion. The process of teaching and testing of BP neural networks to characterize the defect signals was analyzed. The adjusting method of network output weight and rate of learning based on Levenberg-Marquardt algorithm was also discussed. In order to obtain the perfect structure parameters of network and research each parameter's effect on the performance of network, a large number of simulation tests were conducted. The MFL data processing experiment proves that the defect recognition neural network system has high convergence speed and good ability of approaching defect features. [ABSTRACT FROM AUTHOR]