Efficient Plant May 2022 - 12

feature | legacy assets
feature
decisions about asset replacement based on
gut feel. Real-time data analysis technologies
overcome any subjectivity in the way
machines are operated and enable asset
replacements to be planned more
eff ectively, based on real data rather
decisions about asset replacement based on
gut feel. Real-time data analysis technologies
overcome any subjectivity in the way
machines are operated and enable asset
replacements to be planned more
eff ectively, based on real data rather
than instinct.
Th is is where Industry 4.0
Th is is where Industry 4.0
technology can help. By installing
relatively low-cost sensors in the
right places on existing machines,
you can collect and analyze data on
energy effi ciency, productivity, and
maintenance costs.
technology can help. By installing
relatively low-cost sensors in the
right places on existing machines,
you can collect and analyze data on
energy effi ciency, productivity, and
maintenance costs.
Th is provides essential information for
your cost analysis, helping to inform investment
decisions. Using real-time analytics,
machine learning, and AI plays an important
role in maximizing the lifespan of legacy
assets and informing the transition to new
equipment at the right time, " stated Adam
Lea-Bischinger of MCP, Chicago (mcp-am.
com), an asset-management consultant
organization.
By combining and analyzing multiple
data feeds, predictive-maintenance soſt ware
helps to build a single version of the truth
about each piece of equipment. It enables
manufacturers to better understand and
optimize performance, as well as accurately
predict the remaining useful life of equipment
so that its replacement can be planned
and budgeted for in a controlled way.
Th e operational effi ciency and targeted
maintenance benefi ts achieved by using
predictive-maintenance technologies in
the manufacturing environment have
been found to extend asset lifespan by
as much as 50%.
USE CASE
A global leader in bauxite, alumina, and
aluminum products, Alcoa Corp., Pittsburgh,
PA (alcoa.com), deployed predictive-maintenance
technology at one of its
zero-waste-to-landfi ll aluminum smelting
12 | EFFICIENTPLANTMAG.COM
Sensors and Industry 4.0 technology can extend
the life of legacy assets while improving overall
operational sustainability.
and operate their machines and facilities,
the greater the carbon-emission reductions
and associated sustainability benefi ts.
By enabling plants to run optimally
for extended periods without
interruption and helping to ensure
machines are operated and maintained
effi ciently and consistently,
machine reliability and performance
solutions help organizations deploy
successful and sustainable maintenance
programs. Predictive-maintenance
capabilities, enabled by Industry
4.0 technology, deliver:
plants. Th e predictive-maintenance soſt ware
was connected to Alcoa's existing machine
and maintenance systems to monitor operational
and critical machinery. By analyzing
machine condition indicators against historical
information, the machine-learning
technology was able to automatically provide
maintenance engineers with alerts and
diagnostics before any functional failures.
As a result, the company has seen a
20% reduction in unplanned downtime,
a reduction in maintenance costs, and
improved operational effi ciencies that have
helped extend the lifetime of its assets. Th e
focus on sustainability from the machine
level upward has allowed Alcoa to develop
new lines of reduced-carbon products
using their innovative Elysis carbon-neutral
smelting process.
PREDICTIVE MAINTENANCE
Th e benefi ts of using data-driven manufacturing
to improve environmental performance
and sustainability are signifi cant. Th e
more eff ectively manufacturers maintain
85% improvement in downtime
forecasting accuracy
50% reduction in unplanned machine
downtime
55% increase in maintenance staff
productivity
50% increase in asset lifespan
40% reduction in maintenance costs
40% reduction in inventory and waste
30% improvement in operational effi ciency
20% reduction in spares consumption
15% increase in machinery effi ciency and
energy consumption.
Replacing components before the end of
their useful life is an unseen source of waste
with signifi cant sustainability implications.
Using fully automated machine-health
monitoring ensures assets can operate for
their full working life while avoiding the
unnecessary consumption of new parts. Industrial
machines are a signifi cant expense,
representing billions in investment. Many
machines may only be rated for 10 to 15
years. If you can extend that lifespan and
stretch the initial capital investment safely,
that can be a tremendous saving. EP
Alexander Hill is Chief Global Strategist and
Co-Founder, Senseye (senseye.io), Southampton,
UK. U.S. offi ces are in Nashville, TN.
Download a report on legacy asset sustainability
at https://hubs.ly/Q018GhgV0.
MAY 2022
http://www.senseye.io http://www.alcoa.com https://www.hubs.ly/Q018GhgV0 http://www.EFFICIENTPLANTMAG.COM
Efficient Plant May 2022

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