Similarly, the combination for AA and AI can
be a driver for increased efficiencies right
across the production environment and here
we get into the realm of Big Data Analysis.
AA and AI technologies enable different
machine states to be recorded and analysed
in real time to recognise the current machine
status, detect potential faults on the horizon
and immediately offer recommendations
for actions to the machine operator or
autonomously initiate remedial actions.
Reaping the maximum benefit from this
development will depend on control systems
that not only embed these technologies
but which also provide higher levels of
connectivity. If the full spectrum of data
sources on the plant floor can be connected
to Edge Computing platforms for efficient
processing for example and on to MIS/
MES and ERP systems, then the full benefits
of AA and AI are realised. This level of
integration enables a far greater range of
KPIs to be analysed and so can be used to
drive improvements in overall equipment
effectiveness (OEE).
What we see then, with control systems
built around AA and AI technologies, are
machines that are self-learning and selfoptimising.
The importance of Artificial
Intelligence to the machine control market
cannot be overstated. In addition to
developing products that incorporate a
connection to cloud based AI as a service,
IBM’s Watson for example, Mitsubishi Electric
for one has developed several in-house AI
algorithms and services and is positioning its
developments in AI technologies under its
own brand to reflect its growing importance.
PROCESSING AT THE EDGE
Managing the crossover between
Information Technology (IT) and Operational
Technology (OT) is the next major challenge.
The successful merge of these worlds
needs to address the skills gap that has
traditionally existed between FA experts and
IT departments. Historically the OT layer is
managed by automation engineers who do
not necessarily have extensive IT skills, while
programmers and IT system architects may
not completely understand the automation
world.
It is worth it though – the most recent
technology developments are based on
edge computing, which provides the answer
by bridging the gap between IT systems and
plant level automation. Edge devices can
collect and analyse data from neighbouring
automation systems and make decisions
in real time to influence the production
process.
Using this technology effectively can
provide a huge competitive advantage. It
also creates new challenges: from system
compatibility to data security. On the other
hand, edge computing systems can be
easily interconnected with cloud services
to provide scalable data storage and
management solutions. In this way users
have all the benefits of IT systems, without
storage issues or being influenced by
potential threats.
LOOKING AFTER YOUR ASSETS
Against the backdrop of a desire to increase
OEE by means of digitalisation, there is a
high demand for analysis of extracted data
(data mining) from production. The condition
and operating profile of plant automation
devices and machines for example like a
production robot’s components such as
servo drives can be recorded. This provides
valuable information for example the status
of wear parts and any contamination.
The resulting database information then
enables predictive maintenance strategies
with a significant saving potential in
maintenance costs. To improve these
strategies further, edge computing
technology [as described earlier] is
being used to leverage the value of
manufacturer’s data using advanced analytic
algorithms executed on the Edge of the
shop floor.
Another important category of process
data is the one that is used for traceability
and consumer information, especially in
the food sector. This can be employed, for
example, to prove compliance with the cold
chain or to attach origin information to food
packaging that can be called up via a QR
code. Data collected from PLCs, controls
and drives centrally and processed locally
using edge computing reduces the bill for
storage space in the cloud in addition to
delivering many other advantages for faster
production control and monitoring.
PREDICTING THE FUTURE
AI is certainly playing a key role in
manufacturing, moving from vision
recognition to skill learning and predictive
maintenance for failure prevention,
however it has further scope for providing
operational benefits and efficiencies. When
detecting impending faults and informing
operators how to fix problems for example
we see AI again coming to the fore.
AI is being used to increase the
effectiveness of predictive maintenance
for plant automation assets. Cloud-based
solutions using AI platforms analyse
operational data and can optimise
maintenance regimes based on actual
usage and wear characteristics. Predictive
maintenance for plant automation assets
can of course reduce operational costs,
increase asset productivity and improve
process efficiency.
For further information, please visit gb.mitsubishielectric.com/en/
Issue 45 PECM 29