PART TWO OF A TWO-PART SERIES ON MAKING THE MOST OF BIG DATA
» In its simplest form, predictive maintenance goes a step beyond traditional data analysis, taking
batch data and applying algorithms to predict likely equipment failures based on that data.
Photo courtesy of Rush Truck Centers
Big Data for
predictive
maintenance An approach to establishing a
predictive maintenance strategy
oday, advances in virtually every industry center
around the use of Big Data, the Internet of Th ings
(IoT) or machine learning (ML). It’s easy to dismiss
these as buzzwords or concepts that only apply
to digital worlds, like e-commerce or fi nance. But
the reality is, they infi ltrate the physical world as
well, and any business running high-capital assets
– including transportation – needs to take notice.
Predictive maintenance, which sits at the heart
of Big Data, IoT and ML, is widely considered to be
the obvious next step in not only the transportation
industry, but also in manufacturing, energy,
oil and gas, construction, agriculture, food retail
and more. As increasingly more enterprises start
to embrace predictive maintenance, they continue
to see large wins, expanding their programs
to gain signifi cant advantages over competitors.
Before getting into the potential results, let’s
discuss what exactly predictive maintenance
is and what it means to implement a predictive
maintenance program.
42 Fleet Maintenance | November/December 2017
Retroactive analysis to
real-time prediction
Th e concept of continuous monitoring technologies
with IoT-connected devices isn’t exactly
new. Many players in the transportation industry
already monitor the output of various devices, a
sort of dashboard, high-level view of performance,
or maybe they do retroactive analysis on the data.
In its simplest form, predictive maintenance
goes a step beyond traditional data analysis,
taking batch data and applying algorithms to
predict likely equipment failures based on that
data. More complex predictive maintenance
systems also delve into real-time optimization,
determining the right combination of operating
factors to reduce wear and increase effi ciency, like,
for example, optimal speed based on conditions
for better fuel use.
Th ese real-time predictions oft en allow for the
detection of impending failures that could never
be detected by human eyes or optimization
nuances that could never be inferred by a person
operating a vehicle or vessel. With predictive
maintenance, downtime and repairs are usually
directly tied to likely failure, minimizing cost
– less downtime, less labor time, less chance of
unexpected failure – and maximizing asset life.
It’s clear here how traditional maintenance
techniques – run-to-failure, preventive or some
combination of the two – inevitably mean unexpected
repair, which leads to longer downtime
on top of unnecessary downtime due to regular
inspection. Depending on the size of the fl eet and
type of assets, traditional maintenance – despite
its good intentions – can cost businesses hundreds
of thousands or millions of dollars in downtime
and catastrophic failures over time.
Of course, in reality, it’s not really a question of
traditional versus or against predictive maintenance.
Even with a primarily predictive strategy,
traditional inspections will still be performed –
especially aft er a major event or specifi c weather
conditions, for example. But now, they may be
focused on specifi c areas, functions or parts that
may otherwise have been overlooked, thus saving
time and money and improving eff ectiveness.
In general, the move toward predictive maintenance
- even partially - has proven associated
cost savings.
A closer look
What sets predictive maintenance apart is that
it makes predictions based on multiple, varied
and often intertwined data sources that could
impact a fleet. It’s not just about looking at one
specific data source. Predictive maintenance
is effective precisely because it combines data
from things like IoT sensors; external data from
SHOP OPERATIONS
Traditional maintenance schedules versus predictive
maintenance schedules on a vehicle’s lifetime
By Mike Bukowski, Sales Principal, Dataiku
T
Graphic courtesy of Dataiku