Authors:
Frankly Toro
;
Hassane Trigui
;
Yazeed Alnumay
;
Siddharth Mishra
and
Sahejad Patel
Affiliation:
Aramco, Thuwal, Saudi Arabia
Keyword(s):
Asset Integrity, Flange Joints, Missing Bolt/Nut, Missing Nut, Multi-View CNN, Synthetic Data, Domain Adaptation, Image Classification, Grad-CAM.
Abstract:
Inspection of bolted flange joints is a routine procedure typically done manually in process-based industries. However, this is a time-consuming task since there are many flanges in a typical operational facility. We present a computer vision-based tool that can be integrated into other systems to enable automated inspection of these flanges. We propose a multi-view image classification architecture for detecting a missing bolt or nut in a flange joint image. To guide the training process, a synthetic dataset with 60,000 image pairs was created to simulate realistic environmental conditions of flange joints. To demonstrate the effectiveness of our approach, an additional real-world dataset of 1,080 flange joint image pairs was manually collected. The proposed approach achieved remarkable performance in classifying missing bolt instances with an accuracy of 95.28% and 95.14% for missing nut instances.