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Despite the numerous and easily quantifiable advantages of implementing predictive
maintenance, there are still some who remain hesitant about doing so. Philipp Wallner looks
at what lies behind the scepticism and how to overcome it
enables engineers to
anticipate and respond to
malfunction before it
becomes a problem. It is an invaluable
set of tools to proactively schedule
repairs and minimise overall disruption
to factory-floor operations, which
ultimately benefits the bottom line.
Yet, for all the advantages predictive
maintenance offers, doubts about
whether this technology delivers
measurable benefits still exist in the
industrial plant and equipment industry.
So, what is behind this scepticism?
Some are sceptical because they have
difficulty determining return on
investment and are unclear about
whether they have the right data, or
enough equipment failure data, to
achieve a working algorithm.
Engineers, too, may have an inaccurate
understanding of predictive
maintenance. The term can sometimes
be misunderstood to mean a ‘black
box’ solution, whereby operational data
from machines is fed into an
application and an algorithm provides
the remaining useful life of the
equipment. This representation
disregards the role of domain
knowledge in developing algorithms to
detect and predict downtime.
To achieve more accurate predictive
maintenance algorithms, organisations
should deploy individuals with
expertise in both engineering and data
science. Engineers should work with
data scientists to generate the
necessary failure data from equipment
to improve how algorithms are trained.
The use of simulation software tools can
ease this vital process. The software
helps employees who are not as
experienced in implementing predictive
maintenance with a variety of easy-to-apply
techniques for collecting data and
then training algorithms.
Simulation can help ensure predictive
maintenance algorithms are properly
trained and powerful, even when less
real-world data can be fed into them.
This is important, because to fully train
algorithms, engineers and data
scientists need adequate failure data.
However, data about malfunctioning
machinery is not always available. It is
expensive and inefficient to intentionally
run equipment to fail for the purposes
of collecting such insights, which is why
simulation software models can play a
crucial role by simulating how industrial
assets function in various test scenarios.
This enables teams to produce failure
data that can then be used to train
algorithms, avoiding the need to find or
generate failure data in real-world
maintenance use cases
There are many companies that have
benefited from this approach to
customise predictive maintenance
software to their specific needs. This
includes energy technology company
Baker Hughes and packaging and
paper-goods manufacturer Mondi.
Baker Hughes developed pump health-monitoring
software leveraging data
analytics for predictive maintenance. In
doing so, the oil field-service company
reduced costs related to equipment
downtime by 40%. It also cut down the
need for additional onsite trucks.
Mondi used software tools to create an
equipment health-monitoring and
predictive maintenance application to
identify possible machinery issues
before they occur. It was able to get its
system ready within six months.
These examples show how companies
can overcome the challenges of
predictive maintenance, from not
being able to train accurate algorithms
to not having enough failure data.
Software simulation tools simplify the
process by making predictive
maintenance algorithms more
powerful and ensuring less real-world
data is needed to train them. Software
also enables individuals with less
experience in data science to carry out
different techniques to pre-process
data and train predictive models based
on the data available.