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Smart Solutions for Challenging Environments


Automated Inspection

High-speed Hoist Rope Inspection

In most mining jurisdictions worldwide, daily inspection of hoist ropes is a regulatory requirement. Current practice involves “manual” or human inspection performed while the rope is moved at a reduced speed. This process is time consuming, prone to error and slows down production. C-CORE has developed a machine vision system that automates the inspection process and allows the hoist to continue operating at full speed. The system hardware and software automatically identifies visual defects such as kinks, strand breaks or thinning, and logs each defect with images taken from three separate views.  The logged imagery is then available for review by a human inspector.

The system is designed to be integrated into existing mine operating infrastructure and can be configured to either replace daily inspection or to provide 24-hour monitoring. Multiple systems are in use in Canada and Australia.

Chain Measurement System

Mooring lines are safety-critical systems that are subject to wear and damage from aging and the powerful forces at play in marine environments (currents, waves, storms and  impact against the vessel or the seabed). Damage can result in high repair costs, operational downtime and risk to human or environmental safety.

C-CORE contributed to development of a Chain Measurement System (CMS) that enables in-situ inspection of offshore mooring chains; the system comprises a software application that analyzes images acquired from video cameras mounted in a Remotely Operated underwater Vehicle (ROV) frame.  The Windows-based software developed by C‑CORE analyzes and spatially deconstructs the chain image frame grabs in order to measure distances between specific image features, such as scale bars mounted on the CMS tool frame and adjacent apices.

The major units of functionality developed by C-CORE for this project were: the camera calibration system to remove distortions caused by the camera lens, converting the camera co-ordinate system into real-world units; image enhancement algorithms to enable users to see relevant image attributes; and measurement and reporting of chain features based on user-defined markers placed in the image.