MINING TRUCKS
To get the latest on mining truck predictive analytics, Paul Moore spoke with Colin Donnelly,
DINGO Director of Product Engineering
Q You specialise in predictive maintenance which gives you a unique
insight into equipment performance. How have you seen the maintenance
trends change for fleets of autonomous trucks in Australia based on the
data for TRAKKA?
A A key source of work identification has always been an equipment
operator, from an unusual sound or vibration to a machine responding
differently (harder steering, less breakout force, sluggish hydraulics). The
introduction of autonomous equipment has removed this form of work
identification, so equipment owners/operators need to rely even more on
condition and performance monitoring technologies such as vehicle
sensors, equipment inspections, and fluid analysis, than they have in the
past. The data captured from an autonomous truck is no different to a
regular manned truck, aside from the additional sensors required for
navigation and collision avoidance. So far, the predictive maintenance
processes are effectively the same, minus anything an equipment
operator would log during his shift.
Q How does TRAKKA differentiate itself from onboard maintenance
monitoring packages offered by the equipment OEMs themselves in
terms of capability and functionality?
A TRAKKA ® works in conjunction with OEM onboard monitoring systems
to help miners get the full benefit from them. A key advantage of TRAKKA
is that it can ingest data from any OEM system, allowing a maintenance
team to collect and collate the information from all their machines,
regardless of OEM or equipment type, into one easy-to-use system. It
seamlessly captures data from a variety of sources, including onboard
sensors, oil analysis & equipment inspections, enabling our customers to
analyse, manage and act on this information using one centralised
platform. A big reason that miners approach Dingo is that they are
frustrated with their inability to maximise on their data due to all the
disparate systems in play. Moreover, most OEM systems are not
integrated with a customer’s ERP system, so we are seeing more and
more clients take data from systems such as Minecare, Minestar and
Komtrax and feed it into Trakka. Once it’s in Trakka, impending issues are
identified and quickly turned into work orders in systems such as SAP,
Oracle, Ellipse and Dynamics AX through Dingo’s proprietary integration
platform. End users don’t want to waste time transposing data from one
system to another. They want to see all their condition monitoring data
and work history in one platform.
Q For mining trucks specifically, what are the key maintenance
parameters that mines want to keep on top of?
A Our larger customers are consistently tracking data from onboard
sensors, oil analysis and equipment inspections. The main focus is
always on the costliest systems such as engines, wheel motors,
differentials/final drives, transmissions and suspension. The most critical
parameters to track vary by system:
n For engines, our customers tend to focus on sensors measuring
temperatures (exhaust, turbo, coolant) and pressures (filter, oil, boost),
as well as oil analysis parameters related to wear and abnormal
combustion.
n For differential/final drives and wheel motors, most of the attention is
on oil analysis for wear and ferrous materials, and visual inspection
(photos) of magnetic plugs. Wheel motors sensors reading temperatures,
speed and power have proven effective in identifying issues.
34 International Mining | MAY 2019
n For transmissions, miners are looking at wear and particle counting
from oil analysis, filter and screen inspections for clutch material and
measurement of clutch shift/slip times
n The suspension system is measured via suspension cylinder pressures
and the overall truck rack and pitch as it moves down the haul road.
Q How popular is your DINGO Managed Solution offering in terms of
mining operations and for truck fleets in particular?
A A meaningful portion of Dingo’s customer base utilises our Condition
Intelligence expert service because of their deep functional and subject
matter expertise, and trucks tend to be a focal point due to fleet sizes
and the potential for significant cost savings. However, very few clients
currently use the full managed solution. It was popular as recently as a
few years ago as the mining industry was evolving in the realm of
condition monitoring, but today, most mid to large scale mines have a
skilled team of resources who use TRAKKA to run their predictive
maintenance programs. In our experience, junior to low level mid-tier
miners typically don’t have access to the same level of resources, so
these mines stand to benefit the most from Dingo’s managed solution.
We have helped a number of these smaller mines transition away from
costly maintenance and repair contracts.
Q More and more mines are installing LTE networks; does this mean
customers will be able to get greater real time capability from TRAKKA?
A One of the limiting factors in getting real time data from equipment has
been the communication systems at mines. As they improve the coverage
and bandwidth of these networks, it will allow the vast array of data
being collected and stored on the machines to be communicated to
TRAKKA. While we don’t intend to replace real-time monitoring systems,
TRAKKA will use this information to predict potential issues even earlier
and recommend corrective actions faster. Our data scientists will also be
able to use this information to update TRAKKA’s predictive analytics
models more rapidly than we are able to now, which will further improve
decision-making.
Q While you have been a trailblazer in predictive maintenance, the market
does not stand still, what is the “next level” in terms of TRAKKA
capability and functionality?
A Most of our customers have invested heavily in fleet management
systems, so they can make decisions about immediate equipment issues
(alarms going off in the cab) or dispatching equipment to optimal loading
face. However, there is still a significant opportunity to harvest and use
all a mine’s condition monitoring information to create the proverbial
crystal ball for maintenance. Dingo has over 25 years’ worth of condition
monitoring data on mining equipment, along with the corresponding
maintenance recommendations, work performed, and outcomes. We are
working with data scientists to tap into the power of this data and
develop machine learning models that will automatically deliver the
optimal corrective actions, detailed work instructions, and associated
confidence levels. The beauty of these models is that they will get
smarter and smarter as we continue to feed them new information. If we
can get this right, miners will have the power to anticipate the future and
proactively manage equipment maintenance, while reaping huge
productivity, planning and safety benefits in the process.