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