Abstract
Usually, substantial part of a mine haulage system is based on belt conveyors. Reliability of such system is significant in terms of mining operation continuity and profitability. Numerous methods for conveyor belt monitoring have been developed, although many of them require physical presence of the monitoring staff in the dangerous environment. In this paper, a remote sensing method for assessing a conveyor belt condition using the Terrestrial Laser Scanner (TLS) system has been described. For this purpose a methodology of semi-automatic processing of point cloud data for obtaining the belt geometry has been developed. The sample data has been collected in a test laboratory and processed with the proposed algorithms. Damaged belt surface areas have been successfully identified and edge defects were investigated. The proposed non-destructive testing methodology has been found to be suitable for monitoring the general condition of the conveyor belt and could be exceptionally successful and cost-effective if combined with an unmanned, robotic inspection system.
Highlights
IntroductionA typical conveyor consists of drive unit(s), conveyor route, thousands of idlers, meters of belts and other auxiliary devices
The point cloud data was acquired with the laser scanner from 5 locations, situated in the lab along the belt conveyor (Figure 9)
Images were captured with a digital camera, attached to the Terrestrial Laser Scanner (TLS)
Summary
A typical conveyor consists of drive unit(s), conveyor route, thousands of idlers, meters of belts and other auxiliary devices. All these elements have been considered as interesting objects from optimisation, testing and predictive maintenance perspectives. Maintenance of belt conveyor elements have been focused on drive units diagnostics, namely pulley’s bearings [1], gearboxes ([2], see review [3]), idlers [4,5,6,7], belt diagnostics [8,9,10,11,12,13], belt wear [14], belt optimisation [15] and others (efficiency, control [16]). One may use SCADA systems [18], portable devices based on termography [19], vibration measurements [1,2], Internet of Things components [20] or robotic systems for inspection [21,22,23]
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