BIG DATA AND DIGITALISATION
utilise a maintenance management solution that
approaches data management and analysis from
a problem-solving perspective. While collecting
‘all data, all the time’ is impressive, collecting the
right data at the right time is necessary to
analyse these datasets in a way that quickly
identifies results-driven actions.”
Acquiring telemetry data from assets
pertaining to the engine, chassis, drive system,
tyres, and even related oil analysis, in real time,
arms maintenance teams with plenty of raw
information to evaluate component health. From
there, they can project the component’s
estimated remaining useful life, or even take
immediate action (repair or replace) to mitigate a
catastrophic failure.
Modular argues that when considering the
health of equipment components, maintenance
departments must identify high-impact failure
modes, which includes:
n The failing component
n The root cause of the component’s failure
n Component health degradation trends for the
failure type (is the failure detectable earlier
on the P-F curve?)
n Actions to take to prevent future failures, or a
plan to replace the component before it fails. can send the data you need now (the ‘right’ data)
as a higher priority to better prevent catastrophic
failures, while still collecting and transmitting the
rest of the data (the Big Data, not required now)
in the background for longer-term processing and
analysis.
“For example, if a haul truck’s engine oil levels
are critically low, a maintenance technician will
receive immediate notification. This real-time
information drives technician action to stop the
unit and address lubrication levels before major
engine damage occurs.” While intervening to
avoid this catastrophic failure will deliver direct
and often-significant component cost savings,
other indirect yet substantial cost savings can
also be realised:
n Reduction in required personnel and material
resources, since it takes far less labour time
to address the lubrication levels than it does
to replace major engine components
n Potential avoidance of interruptions to
operations, as a down truck may block route
access if it fails in a critical path area of the
mine
n Avoidance of interruption to planned work,
since units with planned maintenance now
fall to a lower priority or delay, increasing
Van Wegen adds: “An effective maintenance
management system provides plenty of data to
confidently identify the above, and some of the
recently released systems can often process this
data in real time by two different technological
means: edge computing and cloud computing.
When utilised the right way, both of these
computing means will help increase equipment
availability, minimise troubleshooting efforts,
mitigate catastrophic failures, and provide
analyses to identify component health
degradation and predict when potential failure
points will occur. These technologies also facilitate
necessary changes to procedures or maintenance
plans in a timely manner, helping to improve the
visibility of operational data across the mine.”
So-called edge computing, the best option for
processing real-time data information streams,
occurs at the equipment level to reduce latency
and process algorithms locally without the need
for high-speed internet. “This serves as a
mechanism to quickly notify operators and
maintenance teams about critical, time-sensitive
conditions while also enabling rapid data
transmission for central processing. The logic
that would otherwise require time to send data to
the cloud, run analysis, and push notifications
back to an operator can now live in the
equipment’s on-board mobile system, essentially
learning from big data analytics, and immediately
feeding this logic back to the edge to prioritise
critical issues and accelerate proper actions
where and when they’re needed.”
Modular states that edge computing devices their potential for failure
n Reduction in technician hazard risks and
associated liabilities, as remote analysis
reduces on-premises, physical analysis of
components.
18 International Mining | NOVEMBER 2017
Cloud computing and Big Data
Since Big Data generally requires some amount
of analysis, an ‘all data, all the time’ streaming
capability is less suited for edge computing than
it is for cloud computing; the Big Data priority
focuses more on analysis than it does immediate
message transmission. Furthermore, stream ing
large volumes of data requires massive storage
and advanced analytics capabilities,
unfavourable to edge computing.
Some maintenance management systems
employ cloud computing, in addition to edge
computing, to manage the large quantities of
real-time and batch data that could have any
influence over a component’s health, spanning
as many different informational points as
possible.
Van Wegen from Modular adds: “Cloud
computing’s scalable mass collection and data
storage facilitates long-term analysis. This
enables mines to improve their maintenance plan
by reviewing predictions of a component’s
remaining life, and provides a feedback loop to
the edge configuration as new trends are
discovered or old trends are disproved. The
cloud’s vast resource capabilities process heavier
computing algorithms to predict potential issues
related to component health, helping to improve
root cause analysis of component failures.”
This Big Data management concept is not
new, but mining companies have only recently
started focusing on the information and
required actions behind the data they gather.
“The more data we can collect, and the more we
can learn about how components operate and
fail, the more opportunity to replace
components based on their condition, rather
than a fixed time interval. Many maintenance
teams will replace components even though
they are still in healthy condition and could
have run for many more hours simply because
the planned maintenance schedule dictates
their replacement. While OEM guidelines specify
replacement at certain intervals, actual
operating conditions and duty cycle can often
extend equipment lifetime well beyond those
specifications. One company proved this when
they ran an engine until its condition required
replacement, rather than replacing it at the
OEM-specified 18,000 hours. The company
monitored critical parameters such as blow-by
pressure to ensure optimum engine life
extension without running the component to
failure, and extended the engine life by an
additional 22,000 hours.”
Cloud computing’s massive storage capability
also facilitates easier machine learning and
analytics. Gone are the days that require an
engineer to sift through huge raw data files;
cloud computing feeds large datasets into
intensive machine-learning algorithms that can
identify trends, calculate predictions about
component failures, identify components that are
operating differently than other components with
similar operating hours, and much more. “In the
end, these complex algorithms and constantly-
evolving data models provide maintenance
departments with trusted information about
component health, facilitating more planned
maintenance instead of costly unplanned efforts.”
An example of this cited by Modular comes
from a large mine in Australia, which reduced its
unplanned events by about 25% in roughly a
year’s time after implementing an intelligent
maintenance management system that utilises
these complex algorithms. “Considering that
unplanned maintenance is typically three to 10
times more costly than planned maintenance, the
shift to a more planned schedule saved the mine
significantly in equipment downtime, as well as
in component and labour costs.”
Cloud computing technology also facilitates
remote maintenance management; by monitoring
equipment and components remotely,
maintenance technicians no longer have to
inspect equipment on site. This remote
capability allows maintenance personnel to
diagnose a component issue before they go to
repair it, arming them with all necessary tools