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Corrosion Detection using Surface Images for a large Petrochemical Plant

Business Objective:

Processing of huge numbers of images using computer vision for predicting the severity of corrosion or fatigue and recommending preventive actions.

Key Solution:

  • A pilot was conducted using surface images of the assets and structures, taken at different times during the inspection, maintenance or shutdown/ turnaround in last few years.
  • The images and associated data were ingested into tcg mcube and placed in the contextual form based on the classification of assets and structures.
  • Part of the images and associated data were used to train the computer vision AI model, while rest was used for the testing.
  • It was observed that the condition of bad images and related severity of corrosion or fatigue were predicted with an accuracy of more than 90%.
  • A recommendation engine was also developed using associated data points and business feedback/rules.
  • The engine provided recommendations like repaint or part/ section repair or on high severity, even replacement.
  • Data Used
    • 9K+ Images of equipment (tanks, heat exchangers, columns, reactor, separator/ surge drum), pipes, valves, fittings, flare stack, pipe support, structures of plant.
  • Technology Used
    • Digital camera, user interface to upload images to tcg mcube
    • tcg mcube – computer vision (R-CNN, DNN)


Business Impacts & Outcomes:

  • Severity of corrosion or fatigue to the assets or structures was reported based on the images
    • Low severity (closer to 0.10) means, not much corrosion or damage has occurred to the metallic surface
    • Medium severity (closer to 0.70) signifies the asset/structure needs repair
    • High severity (closer to 0.99) means replacement is required
  • Based on level of severity, insights were provided on factors contributing to the damage (RCA)
  • Recommendations like ‘No Action Needed’, ‘Repair’ or ‘Replacement’ was also provided through the system