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The Energy and Utilities booklets Business Challenge #2
Predictive Maintenance:from « fail & fix » to « prevent
& predict »
In light of the major changes in the Energy and Utilities
sector, ensuring the continuity of operations is essential in all
circumstances. By identifying the risks of failure thanks to
detailed knowledge of the equipment, predictive maintenance
addresses this challenge. It promises an optimised production
model.
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OVERVIEW
A sector facing major changesCompetition from newcomers, growing
energy demands, self-consumption, climate change, outdated
facilities, regulations, smart grids, smart cities, smart
buildings... Players in the en-ergy and utilities sector are
currently facing major changes. Electricity, gas and water are now
considered com-modities; their valuation no longer lies in their
access, but in their use and related services. For instance, the
dy-namic distribution of power supply in a neighbourhood, the
remote control of a building’s comfort temperature or ensuring the
proper operation of a wind farm.
These services require optimal management of production,
transmission and distribution equipment, while focusing on cost
reduction. Intervening on an asset according to its actual
condition rather than according to a provisional schedule is one of
the challenges of predictive maintenance. After curative and
preventive maintenance, this new approach is based on in-depth
THE ENERGY AND UTILITIES BOOKLETS
knowledge of the equipment to accurately anticipate failure
risks.
Predictive maintenance takes advan-tage of the digitalisation of
equipment. When connected to sensors, the latter emit weak signals
concerning their operating condition. After processing, the data
collected is compared to fai-lure ‘signatures’ modelled by
predictive al-gorithms based on historical device malfu-nction
informa-tion. When an equipment sta-tus approaches a signature, a
real-time alert is sent to initiate remediation before failure
occurs.
TAKING UP THE CHALLENGE
Predictive maintenance,a corporate programmeThe challenge is,
therefore, to have the most reliable detection rate possible,
with an alert threshold that is fine-tuned over time through
machine lear-ning. The integration of external data, particularly
meteorological data, can also help measure the influence of the
immediate environment on the perfor-mance of the equipment to adapt
pro-duction accordingly. This anticipation makes it possible to
plan interventions
as closely as possible to needs in or-der to avoid unnecessary,
costly actions by techni-cians, to op-timise spare parts logistics
and prevent drops in pro-ductivity by
reducing the risk of unplanned ou-tages. For a connected site,
detailed equipment status knowledge and ear-ly failure management
are expected to reduce consumption costs and im-prove
productivity.
Choosing the objects to connect, qua-lifying the data to be
processed, se-lecting the communication protocols,
Governance, data and algorithms, technological platforms,
progressive deployment, change management... A predictive
maintenance project is designed as a true corporate programme.
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630 billion dollars.
According to a study by McKinsey*, this is the amount that
companies are expected to save by 2025 thanks to predictive
maintenance. Reduced maintenance costs will account for 10 to 40%
of savings, the number of failures will be cut in half and the sums
invested in new machines will decrease by 3 to 5% thanks to their
increased lifespans.
* https://mck.co/2wWQyXp
REFERENCE POINT
validating the analysis algorithms, dashboards, etc., all
require the com-bination of business, telecom and IT (IS, UX/UI
Design) expertise in a co-construction approach. A predic-tive
maintenance project is part of a corporate programme and its many
initiatives: project management, data management, the assembly of
techno-logical building blocks, industrial roll-out, change
management (bringing together teams that do not usually meet, to
work together), starting with the requisite corporate programme in
collaboration the Executive Com-mittee. The programme is all the
more complex as it is part of the company’s global
digitalisation.
USE CASE 1
Combating leaks
In France, leaks account for, on average, 24% of treated and
transported water, and up to 50% in some places (source:
Observatory on public water and sanitation ser-vices*). To combat
this loss, one network operator deployed acoustic sensors every 800
meters on its pipes. ‘Listening’ to the network makes it possible
to detect new leaks and take appropriate action, thereby increasing
efficiency.
* https://bit.ly/2sFmxX7
USE CASE 2
Maintaining energy production
A wind farm operator installed vibration sensors to detect
anomalies in mechanical components (gearboxes and generators) to
prevent the risk of sudden failure and maintain its electricity
production. A more detailed analysis resulting from the
combi-nation of these data with other factors (wind speed,
temperature, etc.) makes it easier to make choices that increase
the lifespan of the wind turbines.
USE CASE 3
Optimising networks thanks to consumption data
Smart meters, such as Linky in France, provide a real-time view
of energy consump-tion by area, right down to the electrical
equipment in a residence. Beyond load ba-lancing on its network,
distributors can use this granular knowledge to offer consump-tion
information and alert services should an electrical device fail.
The result: reduced consumption and customer satisfaction and
loyalty.
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Gfi Informatique is one of the most successful digital service
companies on the market with continuous growth since 2011. With
19,000 employees, 1,500 million euros in turnover (proforma) for
2018 and a presence in more than 20 countries, Gfi Informatique
supports its clients throughout their transformation projects.
THE ENERGY AND UTILITIES BOOKLETS
“Competition forces energy producers and industrialists
alike to ensure that there are no interruptions in
production or services on their sites. By having granular
data on the operation of their equipment, they can
anticipate fluctuations in production, and fine-tune the
use of machines according to external parameters such
as the climate. Predictive maintenance is the Minority
Report of maintenance.“
As an end-to-end integrator of all technological building
blocks (IoT, Big Data, Analytics, AI, Data Visualization,
UX/UI Design, portal, mobile, API and IS integration)
across the entire value chain, Gfi assists companies in
their digital transformation by leveraging Cumulocity
(Software AG), Microsoft Azure IoT and SAP Leonardo
platforms and the Big Data Hadoop processing
environment. From consulting to data acquisition and
visualisation, as well as data processing, enrichment,
storage and export, Gfi stands out for its ability to offer
an industrialised, agile, made-to-measure model.
#Observer Voice #Gfi Solution
François Boncenne, Director of the Energy,
Utilities and Chemicals Sector, Gfi Informatique
CONTACTPhilippe Guillen
Global Solutions Manager - secteur Energie & UtilitiesGfi
Informatique
+33 (0)6 08 84 31 96 [email protected]
gfi.world
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