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rowing numbers of companies
are using the potential of IoT and
Big Data to anticipate wear and
tear and mechanical malfunction of their
equipment. This predictive maintenance
process is becoming increasingly accurate
thanks to machine learning capabilities.
Whether for controlling machines remotely,
monitoring their operation or simulating
production processes, many manufacturers
are now turning to the Internet of Things
(IoT). Already an indispensable technology
in Industry 4.0, IoT enables communication
with a great variety of objects, from fork-lift
trucks to chemical sensors.
How does it work?
The installation of connected sensors in an
analytical program enables the constant
monitoring of a component, a piece of
machinery or a system. How? In various
ways, including measuring temperature
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INTELLIGENTCIO
The data collected by these sensors is
analysed to define a machine’s standard
operation. An anomaly is detected by
comparison with the benchmark operation.
If an anomaly is detected, the maintenance
agents are alerted and as such can possibly
intervene before the machine breaks down. showed that 70% of 450 IT decision-makers
and on-site service managers do not know
exactly when their equipment needs to
be maintained or upgraded. 46% of their
machines’ unplanned outages are due
to component failures. And what are the
consequences? Unexpected shutdowns,
lasting four hours and costing US$2
million on average, a significant impact on
production, IT and customer services.
Better prediction for greater savings This affects every sector!
To boost their ability to anticipate wear
and tear, more and more companies are
now using the combined potential of IoT
and Big Data. For companies – because
equipment outages are expensive – the key
points are real-time monitoring of machine
performance and significant savings. Predictive maintenance, the spearhead
of the ‘connected plant’ for detecting
potential equipment malfunctions on
assembly lines, is now being used in all
spheres of activity. For example, elevator
manufacturer Kone has set up a partnership
with IBM to fit its elevators with sensors
to detect and anticipate malfunctions.
Operating data, stored on IBM cloud
servers, are processed by the cognitive
through infrared images, airflow pressure, or
vibration frequency.
A survey conducted in three European
countries and the United States in 2017
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