Defects tracking via nde based transfer learning

Subrata Mukherjee, Xuhui Huang, Yiming Deng, Vivek T. Rathod, Lalita Udpa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020
EditorsIndranil Roychoudhury, Jose R. Celaya, Abhinav Saxena
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263059
DOIs
StatePublished - Jun 2020
Externally publishedYes
Event2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020 - Detroit, United States
Duration: Jun 8 2020Jun 10 2020

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Volume2020-June
ISSN (Print)2325-0178

Conference

Conference2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020
Country/TerritoryUnited States
CityDetroit
Period06/8/2006/10/20

Funding

This work is partially supported by the US Department of Transportation Grant: Low-Variance Deep Graph Learning for Predictive Pipeline Assessment with Interacting (Award No. 693JK31852A01).

Keywords

  • Bivariate function
  • Magnetic flux leakage
  • Mixture regression
  • Nondestructive evaluation
  • Transfer learning

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