With an increased collection of data, assessing the health of an asset and designing recommendations or executing response actions via prognostics and health management (PHM) has made great advances. These actions can be corrective or preventive depending upon the risk of failure or the cost of repair. As downhole testing tools operate in extreme environments, they are subjected to conditions like elevated temperature, shocks, vibrations, and pressures. The dump mandrels used in the process are prone to wear and tear like scratches, pits, and corrosion, which may cause operational failure. If these damages and their degree goes undetected and no remedial actions are taken, possibilities of non-productive time (NPT) and financial losses increase drastically. This paper aims to develop a fitness inspector which uses Computer Vision and Deep Learning to identify critical surfaces of these tools and the damage within them. This will help the Subject Matter Experts (SMEs) by replacing the qualified workforce provided by them and reducing the time consumed to gauge the health status of all the tools as the diagnosis can be made in real-time.
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Critical Surface Identification, Damage Detection, Deep Learning, Computer Vision
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