FATIGUE MANAGEMENT location , speed calculation etc . Instead DSS taps into the machine data for those parameters . And of course the underground network needs to be good enough to allow the sleep event clips to be sent in real time . If not then there will be a delay until the machine passes a relay point . The underground FMS market really began around 2017 and has still ramped up fairly slowly since then . Cat DSS started underground in Australia and has expanded to other mining regions since then , particularly through the 100 % site deployment agreement with Newmont such as at Porcupine Gold Mines near Timmins , Ontario which includes the Hoyle Pond underground mine ”
Looking at non haul trucks – Cat has an agreement with Seeing Machines Pty Ltd to deliver and support light vehicle and on-highway driver fatigue and distraction monitoring Cat ® dealers . Seeing Machines ’ Guardian 2 system replaces the Cat DSS-H and is “ an advanced , non-intrusive system that senses operator movements and analyses them for symptoms of fatigue or distraction in light vehicle applications .” Similar to the off-highway DSS system , seat vibration and audio alarms alert operators when a microsleep or distraction event is detected to effectively reduce dangerous and costly incidents . Guardian 2 joins DSS in the Cat MineStar™ Detect portfolio of safety technologies and services .
Light vehicles work as they are being driven from A to B like a large haul truck such as in and out of the pit . But most other heavy equipment types in the pit have their own peculiarities in terms of how the operator is working . Water bowsers aren ’ t being used continuously so there aren ’ t the same fatigue issues but again as they are a form of truck FMS is now being applied . Shovel and dragline operators are in a roomy aircon cab and the machine is not that mobile beyond the swing when operating . And the operator on the biggest units are often looking down through a floor panel window . Dozer operators are generally doing specialised tasks on stockpiles or wastepiles . Some of these roles may be more suited to wearables but there still isn ’ t much of a market where it doesn ’ t involve a truck of some kind .
“ Looking at graders as an example , the operator given the nature of the machine task is looking obliquely most of the time . The tech would work but maybe only 25 % of the time when they happened to be looking in the right place . Seeing Machines is looking at a system with multiple inputs based on multiple cameras for these types of applications but of course that means a higher cost .”
Of course , one reason why the big trucks came first was that this was deemed to be the area most in need of FMS for relative risk reasons . But also the relative cost of fitting out a whole fleet of high cost machines was minor . But as technology has evolved customer awareness of fatigue has increased so the interest has moved beyond just haul trucks to the light systems so lower cost , less physically robust systems have been developed for those vehicles and many customers are now installed light systems at sites where the haul trucks already have DSS . The same applies to coming back and putting it on nonprimary fleets such as 100 t class trucks used for earthmoving tasks or even ADTs . And putting DSS on contractor operated trucks is becoming more widespread .
Finally , to what extent is FMS being required via regulation . “ Taking the US and Canada as an example , we see most regulators wanting mines to do something about fatigue but haven ’ t reached the point yet of requiring it . The most common situation today is a mine being required to have a fatigue management plan but there not being many specifics about what has to be in that plan from a technology point of view . In a way it is good that is isn ’ t a legal requirement because the mines are doing it to be safer not because they have to tick a regulator ’ s box .”
Fatigue Science goes to the enterprise level
Fatigue Science is based in Vancouver , BC , Canada and describes itself as " a leading provider of predictive human performance data in heavy industry , building software that leverages scientifically-validated biomathematical models in order to quantify and predict the cumulative effects of sleep disruption on human reaction time and cognitive effectiveness .” Most recently , it launched 14-Day Fatigue Forecasting which it says is a breakthrough advancement in fatigue management technology is a powerful new addition to the company ’ s Readi™ Enterprise Suite software platform . “ Readi Enterprise Suite , the Fatigue Management Information System from Fatigue Science , is widely relied upon for its ability to provide objective historical and real-time visibility into workforce fatigue . Now , the release of 14-Day Fatigue Forecasting expands this visibility , providing the world ’ s first 360 degree view of fatigue – past , present , and future .” With this advancement , FS says proactive planning measures and proactive safety critical actions that were previously impossible are now achievable .
On the market for fatigue monitoring in mining , CEO Andrew Morden told IM : “ Mines see complex tasks being carried out on long shifts in remote locations with significant safety risk so fatigue monitoring is being seen more and more as a must have in safety and operator health and wellness assessment . Also , there is a revenue aspect as better productivity of operators at mine sites can make a huge difference in the bottom line over a relatively
Fatigue Science ' s Readi™ technology is most commonly now applied on Fitbits short time period when you talk about an extra truckload per shift for example . Mines are also used to dealing with data . We are at a point now where we are even correlating things like spot times and dig rates with fatigue which has been really powerful for our clients plus gives a quantifiable ROI for them in investing in our technology . So it is no longer just about safety – its about production efficiency as well .” He adds : “ It does seem that fatigue management and the use of fatigue data is most advanced in the oil and gas industry but mining is not far behind . We have some traction now with two global Tier 1 mining houses , both of whom have rolled out major Readi deployments . Of the top 25 mining companies we have or have had projects with upwards of 10 of them which could include a pilot or risk analysis of their fatigue data using our software .”
That said , the company due to NDAs hasn ’ t said much publicly since it announced the rollout of 1,000 ReadiBand devices at the Peñasquito gold mine in Mexico in 2019 ( now Newmont but then Goldcorp ). But FS says this is also as it has transformed itself from focussing on the physical Readiband product to the Readi enterprise platform approach – an IIoT solution – providing predictive analytics and forecasting future fatigue risk profiles for operators .
Morden says : " Our mining customers tell us they want to be proactive as well as reactive . We complement the likes of a DSS by giving the mines insight into when their operators are likely to fatigued in the near future – up to 14 days in the future .” Some FS customers are now monitoring fatigue hotspots over time so they are able to do things like alter rosters and schedules slightly where necessary . Equally as mentioned elsewhere in this article , primary reactive systems have been dominated by large haul trucks for cost and strategic focus reasons – FS has allowed mines to get an overview of fatigue across the whole workforce , very useful for health and safety managers and senior management while at an individual level the operators can monitor their own sleep and be alerted to imminent fatigue via their wrists .
This transformation to an enterprise approach also means the focus is no longer just on its own Readiband proprietary wearable – Readi technology is most commonly now applied on Fitbits and to some extent Garmins . Up to a point FS says it is ready to integrate with whatever preferred wearable to mine chooses to work with .
VP , Product & Corporate Development Robert Higdon talked through some details on ongoing product development at FS : “ The system starts with a wearable device that is set up for validated sleep data capture and uses SAFTE , our biomathematical fatigue model . At an individual level it is looking back at a person ’ s up to two weeks of sleep data and from that it is looking specifically for the qualities of the sleep that have been statistically correlated as good predictors of fatigue – that includes sleep quality , level of interruptions , timing including circadian rhythms , as well as things like seasonal light
JANUARY 2021 | International Mining 65