A Mining Imperative:Enhanced Accuracy of Real-Time Spatial Data & Reporting

Gary Robertson – Brisbane Conference September 2016

• Commodity prices remain at depressed levels

• Demand is growing to improve mine site operational efficiency and keep mines competitive

• Onsite demands are growing!

• The requirement for accurate day-by-day or shift-by-shift data is applying pressure to current technology and resources

2

So What is the Problem?

LESS:

– Time for turnaround

– Resources to do it

MORE:

– Data

– High Accuracy

– Analysis and Reporting

• Sites needs:

– Higher quality data; More often - throughout the month

– The accuracy of tonnage, volume, and spatial data all need to be improved and monitored in real-time

– “Survey adjustments” at the end of each month to align better the currently available data from weightometers, bucket

factors, & truck counts

• The solution is a system that:

– Generate terrain surfaces on-board excavators, shovels or draglines in near real-time

– Calculate real-time tonnages within the bucket

3

So What is the Solution?

• This presentation maintains that to have higher quality data available throughout the month, the accuracy of

excavator tonnage, volume, and spatial data all need to be improved and monitored in real-time

• In presenting this case, the presentation discusses the integration of on-board guidance systems with mobile

mapping (MM) systems and innovative ways to calculate real-time tonnages within the bucket

4

Introduction

• Real-time high-precision terrain scanning using mounted sensors

• Lasers scan beyond boom point; set to avoid the bucket from the field of

view

• Whilst recording the system compensates boom for bounce & twisting

• Machine operates as normal - Operator swings the machine from dig to

dump (or vice-versa)

• Transforms the LIDAR data to create a 3D point cloud

• Point cloud is filtered to remove unwanted artefacts (dust, bucket and

rigging, etc)

• 1m Grid cells; geo-referenced height map

• All updates stored on local (on machine) database ready for presentation

5

Dragline | Digital Terrain Mapping

• Data is capable of being presented visually

to the Operator in near real-time

• Data can be pushed to desktop and web

applications for further analysis

• Data can be exported in a csv format

• Historical Data can be replayed

6

Dragline | Digital Terrain Mapping

Through innovative ways, inertial measurement units (IMUs), strain gauges, pressure

sensors, and HPGPS can generate real-time terrain surfaces on-board loaders or

draglines

7

Machine Terrain Mapping & Bucket Payload

Test Point

Hydraulic Shovel

Strain Gauge

Rope Shovel

Strain Gauge

Same sensors also calculate real-time tonnages within the bucket

• By sensing the amount of dynamic acceleration (while eliminating

vibration noise), a unit is able to analyse the direction the device is

moving in real-time.

• Using several IMUs and knowing their position compared to a

machines pivot points, a good understanding of the machines

motions can be determined.

• Combining this knowledge with hydraulic pressure sensors and strain

gauges on major components, Payload can be determined.

8

Machine Terrain Mapping & Bucket Payload

Accuracy of Terrain & Payload

These studies were to determine that the:

– Quality data available throughout the month, is similar to that of other surveying hardware and processing

methodologies

– Accuracy of the loader tonnage to that of Original Equipment Manufacturer (OEM) strut-based system

9

Site Based Trials

Digital Terrain Mapping

• Test Region:– 3 separate pits

– Conducted over 1 year

• During all seasons to allow an understanding between:– Dry & dusty conditions

– Rain & fog events

• Using different digging sequencing

• Acceptable accuracy for the site is deemed to be less than 0.3m error in elevation across 95% of the data set

• The results from these tests showed that the DTM system is capable of collecting field data to a standard classed as acceptable, by the mine site

10

Site Based Trials | Digital Terrain Mapping

• Test Region:– 2 separate pits

– Conducted between Oct 15 to Jan 16

• The results from the data accuracy trials showed the system being capable of collecting field data to an accuracy of ±650mm

• Factors impacting the tests:

– Usage of clean-up dozers around the loaders and bucket teeth length

– Further analysis of the system was accurate to the ‘as dug’ surface before a dozer

would commence ‘cleanup’

– Errors in highwall crest positioning also differed due to the act of gravity collapsing

past/behind where the bucket had passed

• Independent teeth position checks, as referenced from the sites total station, showed the true teeth position error was only 228mm for a 450mm bucket tooth and 178mm for a 200mm bucket tooth

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Site Based Trials | Machine Terrain Mapping

0.450.300.150.00-0.15-0.30-0.45-0.60

Median

Mean

-0.010-0.015-0.020-0.025-0.030

1st Q uartile -0.111000

Median -0.010000

3rd Q uartile 0.088000

Maximum 0.449000

-0.030557 -0.027659

-0.011000 -0.008000

0.169403 0.171453

A -Squared 665.61

P-V alue < 0.005

Mean -0.029108

StDev 0.170422

V ariance 0.029044

Skewness -0.95759

Kurtosis 1.32029

N 53115

Minimum -0.646000

A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev

95% Confidence Intervals

Summary for Difference

• The test excavator used the hydraulic sensor payload technology (Argus), for bucket payload measurement

• Each truck load was measured across a set of in-ground weigh scales over 4 days

• 270 “full” haulage units were run across the scales, comparing results from the multiple weighing sources

• The results highlighted that the:

– Use of hydraulic sensor payload technology was capable of determining the correct weight

of material within each bucket

– Error difference between the weight scale values and those calculated by the Argus system

was 2.01%, with a distribution deviation of only 1.5%

– OEM strut based system only had an accuracy of 5.1%, with a distribution deviation of 4.6%

12

Site Based Trials |Argus vs. OEM Struts vs. Scales

197t

One Pass Remaining

Final Pass Added

Truck Scales

Reading

Site Based Trials | Argus vs. OEM Struts vs. Scales

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OEM Struts

200t 225t

200t20t

-14% Difference

to Final Pass

175tArgus 195t

175t20t

-1% Difference

to Final Pass

NOTE: Weigh Scale study was conducted by Transcale

• The conclusion of these trials demonstrated that by combining on-board guidance systems with mobile mapping (MM) a mine site was able to:

– generate real-time terrain surfaces on-board the excavators or draglines,

– calculate real-time surfaces and tonnages within the bucket and

– be accurate to within, 300mm, acceptable in most mining environments

• The payload trials demonstrated that the Original Equipment Manufacturer (OEM) strut-based tonnage payload accuracy, ranged from 3-16% in error, compared to a bucket based (Argus) payload system of less than < 2.5%

• Most importantly all the trials and real-time data generation reduced the sites work load to gather and report Real Time spatial information and improving the time taken to correct mine compliance challenges.

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Outcomes from the Trials | Summary

For further information contact:

www.mineware.com | mwsales@mineware.com

Gary Robertson +61 400 023 968