Fibre optic sensing technology is showing industry-wide benefits with real-time monitoring of conveyor belts to reduce costly unplanned down times. Currently, routine inspections of conveyor belts are carried out by maintenance staff, driving along conveyor belts trying to listen for malfunctioning rollers or using handheld thermal/acoustic devices to identify problems, but this isn’t proving effective.

When conveyors are unexpectedly shut down due to issues with rollers or sudden faults, miners need to locate the roller fault, isolate, dispatch, tag, repair and restart the conveyor – losing valuable production hours.

A team of researchers at CRCMining and the University of Queensland’s Fibre Optic Sensing Application Laboratory (FOSAL) have been developing a Conveyor Condition Monitoring (CCM) system using fibre-optic sensing technology to improve the early detection of conveyor belt faults. This technology uses optical fibre installed on or near the conveyor structure to directly sense acoustic activity in real time, at metre intervals along the length of the conveyor.

CRCMining project leader Mohammad Amanzadeh said Distributed Acoustic Sensing (DAS) hardware is at the heart of CCM. “It is exciting technology which has been successfully deployed in various industries for remote production and condition monitoring of assets such as pipelines, wellbores, boarder securities, traffic flow and railway monitoring,” he said.

This advanced sensing system is capable of constantly monitoring an entire conveyor system for idler roller failures and is being further developed by CRCMining. By pairing this technology with event detection software, changes in equipment acoustic signatures can be identified to alert operators of impending roller failures and breakdowns.

CCM is unique, as the sensor is flexible and robust it transforms a single mode optical fibre into a chain of microphones, measuring sounds/vibrations at discrete points along the length of conveyor system. The fibre is easily installed on the conveyor structure, requires low maintenance, and if the fibre is damaged repair is as simple as re-joining the ends of the optical fibre.

DAS hardware is available off the shelf, however has not been applied to mining conveyors. CRCMining is developing the technology further through various field trials facilitating software and algorithms to address the specific application challenges.

“This technology will be a game changer in conveyor condition monitoring and is set to transform the mining landscape,” Amanzadeh said.

“The researchers are trialling the use of fibre optic sensing technology to identify changes in temperature and noise along the conveyor belt to help detect potentially faulty components in real-time and post processing.

“The first site trial was at Queensland Bulk Handling (QBH) in 2014, and recently our team completed a site trial at Anglo American’s Dawson mine in May 2015.

“In conveyors that span kilometres with thousands of rollers, real-time monitoring has the potential to significantly reduce costs in mining operations. CCM technology enables staff to prioritise maintenance repairs and identify types of faults in real-time along the conveyor belt.

“This will lead to an effective strategy in maintenance during planned down times and will reduce costly down-times, prevent catastrophic events, repairs and delays in production caused by roller and pulleys,” he said.

It is anticipated CCM technology will reduce unplanned down times by 20 to 50% depending on the conveyor system. If a mine conveyor currently runs at a production loss of $150,000 per hour this could escalate to millions lost in production each year due to unplanned stoppages. There are also more savings expected with improving labour, belt damage, safety incidences, and adding robotic solutions for roller change.

The current stage of research is to conduct a more detailed trial and data collection using the DAS technology in an underground mine. The current phase of project is supported by CRCMining, Australian Coal Association Research Program (ACARP), Anglo American, The University of Queensland, OptaSense – QinetiQ, Machinery Automation Robotics, and QBH.