Automatic welding detection by an intelligent tool pipe inspection

C. J. Arizmendi, W. L. Garcia, M. A. Quintero

Research output: Articles / NotesConference articlepeer-review

7 Scopus citations


This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called "smart pig" in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Original languageEnglish
Article number012082
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - 9 Jul 2015
Event11th International Conference on Damage Assessment of Structures, DAMAS 2015 - Ghent, Belgium
Duration: 24 Aug 201526 Aug 2015


  • Data analysis
  • In-Line Inspection
  • Pattern Recognition
  • Pipe weld joints
  • Pipeline
  • Smart Pig


Dive into the research topics of 'Automatic welding detection by an intelligent tool pipe inspection'. Together they form a unique fingerprint.

Cite this