Program Book - Master Brewers Association of the Americas

P-172
Neuro-numerical damage detection of bottle crates by means
of spatiotemporal vibration analysis
ANDREAS KASPRZYK (1), Judith Forstner (1), Rainer Benning (1),
Heinrich Vogelpohl (2), Antonio Delgado (1)
(1) Institute of Fluid Mechanics of the University Erlangen
Nuremberg, Erlangen, Germany; (2) Chair for Food Packaging
Technology of the Technical University of Munich, Freising,
Germany
A reliable, durable and fully automated damage recognition
system for bottle crates is indispensable in the food and beverage
industry, in order to ensure product and working reliability as
well as a smooth operational sequence in the logistics chain.
Also the endangerment of the product and company image by a
damaged product causing potential injury to the customer plays a
crucial role in today’s harshly competitive free-market economy,
basically governed by advertising and price. Additionally, reliable
identification of defective packaging before refilling facilitates a
substantial increase in efficiency for the packing plant and thus
lowers operating costs extensively. Considering a transportation
cycle of 400–500 million crates annually, damaged and/or aged
bundles cause enormous problems. For these reasons a hybrid,
consisting of numerical simulations (based on mechanical vibration
impacts) and artificial neural networks (ANN) was developed
within a project titled “Automatic Selection of Returnable Goods
for the Food and Beverage Industry by Neuro-numerics”. In the
present follow-up research project it is combined with image
processing. This further development of the already existing damage
recognition system is currently carried out by the Institute of Fluid
Mechanics of the University Erlangen-Nuremberg and the Chair
for Food Packaging Technology of the Technical University of
Munich. By replacing the laser-vibrometer used in the forerunner
project an enormous reduction in system costs can be expected. As
a superior result, the mentioned project aims at the conception and
conversion of a before-competitive but practical system equipped
with modern digital real time technology that can be trained on-line
and maintained from afar. In addition the new method contains
several innovative aspects compared to already available damage
detection systems. In contrast to other measurement techniques,
e. g. at pre-defined points, spatiotemporal vibration visualization
is used for damage recognition of mass-produced articles for the
first time. This allows the detection of micro-cracks and hidden
damage at arbitrary locations in crates that current systems cannot
recognize. Furthermore, an excellent detection rate, combined
with an extremely fast diagnosis, is an important target. The
major advantage of the developed system is the fact that attainable
innovations are not limited to the food and beverage industry. Their
spectrum of use extends over all economic sectors that deal with the
production and the quality control of packages. Furthermore, the
achievable innovations are able to supply a substantial improvement
in customer safety and operation reliability. All-in-all the desired
results supply an extremely sustainable basis for the exploitation of
the latent, technical-economical potential, spanning various classes
of business.
From 1994 to 1997 Andreas Kasprzyk apprenticed as a brewer
and maltster at the Paulaner Brewery GmbH & Co KG in Munich.
Afterward he was employed at the Spaten-Franziskaner-Bräu
GmbH as a brewer. In 2001 he began his studies on brewing and
beverage technology at the Technical University of Munich (TUM)
in Weihenstephan. He completed his Dipl.-Ing. (Univ.) degree in
2006. After graduation he began employment with Versuchs- und
Lehranstalt für Brauerei in Berlin e. V. as a scientific assistant at the
Research Institute for Engineering and Packaging (FMV). In 2007
he moved to the University Erlangen-Nuremberg (FAU), Institute
for Fluid Mechanics (LSTM). There he is working on a Ph.D. on
“Damage Detection of Returnable Goods” in the group process
automation of flows in bio- and medical technology.
P-173
Driving value by increasing bottling efficiency—Data based
automatic fault localization
AXEL KATHER (1), Tobias Voigt (1), Horst-Christian Langowski
(1), Peter Struss (2)
(1) TU München, Chair of Food Packaging Technology, Freising,
Germany; (2) TU München, Chair Computer Science IX, Group
MQM, Garching, Germany
Bottling plant machines are designed to keep the central machine
running. Nevertheless plant efficiency-reducing downtime can
occur. Downtime is caused by failures of the main aggregate itself
or because of a starvation or blockage through failures of other
machines propagating along the line. Identifying the responsible
machine is not trivial. Normally machines are connected with
transporters with a buffer function. Because of this, the propagation
of failures varies with the buffered bottles. To increase plant
efficiency the machine causing the most plant downtime must be
identified for maintenance and correction. To save money and
exonerate the staff in the bottling line this identification should be
automated. As a base for automatic fault localization, standardized
data is needed. To assure this a standard for production data
acquisition of bottling plants was developed in cooperation with
the industries. Regarding the results of an international survey
these standards are highly accepted and implemented in the
brewing branch. Based on this data, different approaches were
used. On the one hand an algorithm was developed, which is able
to identify the machines causing the central aggregate’s downtime
as well as the machines which emptied or filled the buffers in an
undesired manner. The algorithm is based on a tree-structure of
the dependencies in the plant. The different branches describe the
propagation of failures. The decision on which way to choose is
made by an analysis of the machine operating states in calculated
timeframes. On the other hand mathematical models of the
components of a bottling plant were built. These models enable the
usage of a so called model based diagnosis (MBD) engine which was
developed at the MQM Group of TU Muenchen. The idea of MBD is
to compare a model of the failure-free operation with observations
from the system. If there exists a contradiction between observations
and model a diagnosis of all possible faults is made. To narrow
the failures down it is also possible to define models of the faulty
behavior of the components. The advantage of this solution is that
only component models have to be developed. With a given system
structure an automated diagnosis can be generated by the generic
diagnosis engine. Both approaches led to good results. Whereas
the pure algorithmic solution shows very good results with partial
responsibilities for downtimes, the MBD solution is more flexible.
In the future it might be possible to use it for other technical tasks
as well. Summarizing one can say that the automated diagnosis
of bottling plants can be realized automatically. The different
paradigms have their individual advantages and offer a great
opportunity for extensions.
Axel Kather (born 1978) studied from 1998 until 2003 at the
Technische Universität München/Weihenstephan. In 2003, he
graduated as an engineer with a Dipl.-Ing. degree in brewing science
and beverage technology. From September 2003 until September
2006 he conducted additional studies in practical informatics and
in 2007 he graduated as a master of computer science from the Fern
Universität Hagen. In July 2003 he started working as a doctoral
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