march2021 - 22

TECH SPOTL IGHT

For example, parts of the world that permit a very broad range of
labels and QR data to be analyzed in order to find efficiencies and
problems, Peterson said. In other parts of the world, there are constraints based upon how much data can be viewed, which can have
to do with labor contracts, government data, governance policies, etc.
" We get into a situation where because we can't get better rules
around data governance, aircraft efficiency, predictive maintenance
and these sorts of things, because we can't get into a broader set of
rules that say, 'This is the broader label set that everyone should be
allowed to work with in order to provide these tools.' You get this
disparaging amount of value depending upon who the airline is,
what government is over them, what labor laws are over them, etc.
It's such a complex problem from a data perspective, " Peterson said.
" Where I see one particular drawback, which could limit predictive maintenance reaching full potential is in data sharing and
access, " Elliott agreed. " Some of the leading players in the industry are starting to work on their own data platforms to get in on
the benefits of sharing engineering data. GE's cloud-based Predix
platform allows third party MRO operators to download predictive
analytical data via the internet, store it within their own systems
and share it with customers. "
Elliott added that Airbus launched its own cloud-based data
platform, Skywise, in 2017, which collects data such as work orders,

spares consumption and flight schedules from multiple sources
across the industry for MRO operators to perform predictive and
preventative maintenance. Early adopters of Skywise included easyJet, Air Asia, Emirates and Delta Airlines, all of which are using the
platform for predictive maintenance.
" However, for this really to work, the data needs to be widely
shared. This is a fundamental hurdle. Airbus, like many airlines
and MROs, will have several different customers, partners, locations and, in most cases, they will use different programs for each
one. This leads to data being siloed and sharing programs being
more internally focused, " Elliott continued.
Agreeing with both, de Bree added, " The downside of predictive
maintenance is that in order to be effective, it requires many players
in the industry to share data. Think of airlines, their maintenance
providers, repair shops, pool parts providers, etc. Each of those
players hold a piece of the puzzle in order make predictive models
work efficiently and correctly. "
A more recent hinderance has been the grounding of aircraft
due to the COVID-19 pandemic - obviously limiting the ability
for data to be collected. " What still remains to be seen is how the
grounding of aircraft globally will affect the accuracy of existing
prediction models. All these models are highly dependent on data
to become and remain accurate. For a lot of aircraft, this data has

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Table of Contents for the Digital Edition of march2021