Maintworld Magazine 3/2021

TRAINING AND EDUCATION
Manufacturing companies want to become
agile companies that react in realtime
to occurring events and make databased
decisions [1]. Advanced information
and communication technologies are
growing in the industrial automation field
and the Industry 4.0 is based on them.
While implementing Industry 4.0 successfully,
the obtainable data should be
prepared and processed in a way that it
supports decision-making. The data may
be useful if the technical requirements
for real-time access are met and if there is
an infrastructure with the necessary data
processing and seamless data transmission.
Another principle for successful Industry
4.0 implementation is that manufacturing
companies need IT integration
to improve data use and increase agility
[1]. IoT cloud computing architecture has
a big role in IoT data management. IoT
data and applications are stored in the
cloud for easy access in any client software
web browser. The cloud computing
architecture suits Industry 4.0 because
of its centralized control accessibility for
various users like managers, customers,
operators, and programmers [2]. The collected
data should be used to understand
how the existing plants are running, to
identify the inefficiencies in production
capacity.
DESPITE THE FAST DIGITAL
DEVELOPMENT, IT IS THE
RESPONSIBILITY OF HUMANS TO
INTERPRET THE OUTCOMES OF
SMART DEVICES AND SYSTEMS.
Cognitive capabilities are needed to
convert the exploding big data to meaningful
insights that further improve manufacturing
processes and functions. In
order to translate the patterns, anomalies
and trends to predictions of remaining
lifetime or future behaviour of an item,
thorough understanding of the asset system
is required. Traditional physical models
are highly complicated and require a
lot of modelling efforts to capture relevant
behaviour. Data-driven models and algorithms
usually use pattern recognition
and machine learning techniques to detect
changes in system states. Qualitative
information like risk and reliability analyses
provide essential information about
the target application. These analyses
could provide cause-consequence chains
that connect failure indication or initiation
pattern or a deviation from a certain
chain of events and link the emerging
event with expected consequences. This
allows the user to make predictions and to
take proactive actions in time. A further
step to add value is to connect the data
with business-related information like
KPIs, life-cycle cost and profit model, or
decision-making situation.
Despite the fast digital development,
it is the responsibility of humans to interpret
the outcomes of smart devices
and systems. In smart maintenance,
smart systems support the technicians
and managers, but do not replace them.
However, the employees working in the
field often lack necessary technological
competence and skills, and a large
proportion of fieldworkers are unaccustomed
to the use of digital technologies.
The employees feel that they lack
analytical skills and the capability to
interpret the data provided by novel
sensors [3]. The technology has evolved
so rapidly that relevant standards (e.g.
EN15628:2014) do not take into account
the existence or use of these technologies
when defining the competence areas
for a maintenance manager. However,
these competence areas can be extended
to also cover the knowledge and skills
that arise I4.0 [4]. As a conclusion, the
maintenance managers are expected to
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