Green Tech Magazine December 2017 en

14
Digital
service
excellence
The term predictive maintenance describes the anticipatory digital
maintenance of environmental technology machines and plants.
It prolongs life cycles and reduces the environmental impact.
Annually the market grows by 20 to 40 %.
“The drive components of all our machines
require oil to function properly. We have defined
criteria for assessing the condition of
power train components by means of components
in the oil and their concentration. In
the case of gearboxes, we know what is likely
to cause bearing damage,” says Komptech’s
Managing Director Christian Oberwinkler.
Customers can send oil samples to a central
laboratory every 250 to 500 operating hours.
Should one of the components show a critical
concentration, the customer receives suggestions
for the necessary measures.
“Machine availability is a decisive competitive
advantage for us because it is vital to our
customers’ business success,” says Oberwinkler
and adds that “the next challenge will
be to integrate manual models for predictive
maintenance into the available digital
tools. In this respect, we are dependent on
the development of sensor technology and
its costs.” Predictive maintenance minimises
maintenance effort and costs. 70 % of the
total operating costs of machines and systems
are incurred during the service phases.
Predictive Maintenance is booming
“PM has established itself as an important industry
trend in the European mechanical engineering
sector. Experts worldwide expect
the market to grow by 20 to 40 % annually
across all industries and fields of application,”
Sebastian Feldmann and Vladimir Preveden
of Roland Berger Strategy Consultants state.
Nevertheless, many companies find it difficult
to arrive at a clear strategy and development
budgets.
“The aviation industry is a pioneer, the automotive
industry is following suit, and railways
are just beginning to do so,” says Helmut
Ritter, Head of Engineering for Bogies
at Siemens Mobility in Graz. Siemens is responsible
for the construction, maintenance
and availability of train fleets. From mid-2018
onwards, data on the condition of certain
components will be provided in order to predict
and correct errors before they occur, to
provide maintenance recommendations or
to inform about the remaining service life.
For the most part, the system uses vibration
sensors.
In order to be able to perform PM, sensors,
data transfer and data processing must be
integrated into a service package. “IT and
sensor technology offer many possibilities;
the topic has gained new momentum with
today’s technical possibilities, because it is
now possible to process and evaluate a lot of
data,” says Ritter. The results of the analysis
can also be used to optimise the logistics for
the necessary replacement of components.
However: It is particularly important – also
with regards to costs – to carry out relevant
measurements for a high data quality and
to interpret the results. This is usually done
by adjusting the error patterns using stochastic
algorithms. “The know-how lies in
the subsequent processing of raw data. It’s
all about knowing what a triggered sensor
means: Does it – in the case of trains – show
problems with a component or an irregularity
on the track, for instance?” Experience
values are just as relevant as knowledge of
the ageing process of materials.
Wolfgang Jilek’s Cartoon – Just in time maintenance
New service models
Franz Langmayr, Managing Director of Uptime
Engineering, points out that “PM can
only be used economically under certain conditions:
in case of high investment value, high
follow-up costs or high availability requirements.”
The more diverse component stress
is, the more profitable an investment will be.