Defects Tracking via NDE Based Transfer Learning

Abstract

Pipe infrastructure systems in service continue to degrade with passage of time. As the defects grow with time, for safety purposes, they have to be inspected periodically for detection of harmful defects. This paper presents development of a novel method for identifying defect growth using dynamically updated transfer learning technique on data from magnetic flux leakage (MFL) sensors. The operation of pipeline inspection gauge (PIG) within the pipeline to collect accurate, low noise readings for defect detection is expensive and time-consuming. Running probes within the operational pipeline produces noisy data. In this paper we consider a less noisy and time-consuming baseline readings within pipelines taken in the beginning. Using the baseline data, our goal is to first automatically detect the defective areas during inspection and thereafter monitor the growth of those defects. Based on the baseline data, a bivariate function was estimated using a function estimation method based on mixture regression framework to compute posterior probabilities of the defects at each scanning point. Thereafter, it is seen that applying direct function estimation with noisy field data on subsequent inspections is not effective. We use transfer learning perspectives by leveraging the defect probabilities and location from the previous inspections, and then consequently update those probabilities based on current data by applying a dynamically updated transfer learning technique. The defect growth is dynamically tracked and characterized with high accuracy and sensitivity.