=Paper=
{{Paper
|id=Vol-1620/paper8
|storemode=property
|title=Situational Handling of Events for Industrial Production Environments
|pdfUrl=https://ceur-ws.org/Vol-1620/paper8.pdf
|volume=Vol-1620
|authors=Julia Pielmeier, Stefan Braunreuther, Gunther Reinhart
|dblpUrl=https://dblp.org/rec/conf/ruleml/PielmeierBR16
}}
==Situational Handling of Events for Industrial Production Environments==
https://ceur-ws.org/Vol-1620/paper8.pdf
Situational Handling of Events for Industrial Production
Environments
Julia Pielmeier*, Stefan Braunreuther, Gunther Reinhart
Fraunhofer Research Institution for Casting, Composite and Processing Technology IGCV
Provinostr. 42, 86153 Augsburg, Germany
Institute for Machine Tools and Industrial Management (iwb),
Technische Universität München, Garching, Germany
julia.pielmeier@igcv.fraunhofer.de
Abstract.
Industrial production environments are complex, volatile and driven by un-
certainties nowadays. Enterprises are striving for flexibility and adaptability to
handle these challenges and remain competitive. Market requirements such as
shortened product life cycles, increasing number of variants, and customized
products lead to complexity in production systems. To be able to handle this
complexity, digitalization like the vision of “Industrie 4.0” can offer different
solutions. In such complex production settings, decision-making and real-time
reactions to events occurring during production processes are one way to handle
the challenges. The approach presented here includes a situational handling of
events for a manufacturing environment. The exemplary implementation of the
method will be carried out by means of a complex Cyber-Physical Production
System (CPPS) at the Fraunhofer Research Institution for Casting, Composite
and Processing Technology IGCV in Augsburg and demonstrated using an ex-
ample of a mass production for CFRP components.
Keywords: Complex Event Processing, Rules, Production Processes
1 Introduction
Digitalization leads to increasing amounts of data describing the status of products
and resources in an industrial production environment. On the basis of this data, in
order to achieve a near real-time monitoring and control of production and logistics
processes, an intelligent processing and analysis of data is required. As a result of this
development, "complex event processing" (CEP) plays an important role for analyz-
ing extensive data streams in near real-time. CEP are methods, techniques and tools to
process events in real-time [1]. For example, fraud prevention systems in the financial
world are based on CEP. Fraud detection in banking and credit card processing de-
pends on analyzing events. This must be conducted in real-time to prevent losses
before they occur. [2]
*Corresponding PhD candidate of Fraunhofer IGCV and Technische Universität Mün-
chen.
CEP is both an industrial growth market and a research area. Despite the first suc-
cessful projects in various fields such as banking, there is still a need for experience
with adaptions of CEP to specific processes like the industrial production environ-
ment. [3]
For CEP applications a distinction can be made between complex events, whose
patterns within the data streams are known a priori, and formerly unknown patters,
which have to be recognized for the first time. In the first case, special event query
languages provide a convenient way to specify complex events and efficiently recog-
nize them. In the second case, machine learning and data mining are applied to the
data sets. [1]
The approach presented here focuses on the second case. Figure 1 shows a sche-
matic procedure of a CEP engine [4].
Fig. 1. CEP Engine
Multistage production processes with sequentially following process steps are
characterized by complex relationships and interactions between the process parame-
ters. Challenges for processing events in these production processes include the large
amount and heterogeneity of the data, the high speed requirement of these events, and
the partially insufficient data quality. Therefore, there is a need to process the occur-
ring events automatically, systematically and promptly. For this to be achieved, the
following objectives have to be fulfilled:
• agility,
• responsiveness and
• real-time capability.
This corresponds to the idea of the Real-Time Enterprise (RTE). The RTE stands
for real-time in the business world. All necessary information should be available at
the right time. [5]
RTE includes the electronic control of internal business processes as well as those
that affect business partners and suppliers. The timely consideration comprises all
projects and properties and their current states interacting in real time.
A higher flexibility is achieved in process management using the Real-Time En-
teripise, whereby the company can respond more quickly. Other benefits include the
cost savings, higher speed and better product quality.
In order to implement RTEs various intelligent and decentralized control approaches
are possible [6]:
• Multi-Agent Systems
• Holonic Manufacturing Systems
• Bionic Manufacturing Systems
• Fractal Manufacturing Systems
• Reconfigurable Manufacturing Systems
• Agile Manufacturing Systems
• Flexible Manufacturing Systems
• Service-Oriented Architecture
• Event-Driven Architecture
When comparing the different management approaches by criteria such as flexibility,
agility, real-time capability, responsiveness and realistic modeling, significant differ-
ences, especially with regard to the application areas and the practicality of the con-
trol approaches, show up.
The current research activities aim to introduce the SOA paradigms in all control
levels of a manufacturing company. Business applications offer their functionalities as
services in a Service-Oriented Architecture (SOA). The communication approach of
SOA is based on request-reply communication.
Since RTE has to react to events in real-time, the implementation of publish-
subscribe mechanism is needed for RTE [7]. An Event-Driven Architecture (EDA)
supports the production, detection, consumption and reaction to events. Nevertheless,
SOA and EDA are complementary concepts for achieving modularity, loose-coupling,
and flexibility [8].
In addition, the systematic processing of events that are generated during the pro-
duction is a promising approach for the implementation of RTE. Although CEP is a
proven technology in the financial industry, the acquisition of real-time monitoring
and control of manufacturing processes requires more attention.
Figure 2 provides an overview of the classification of CEP as a building block for the
realization of a Real Time Enterprises. For this purpose, a distinction is made between
control architectures and software technologies.
A Service-Oriented Architecture (SOA) in conjunction with an Event-Oriented Ar-
chitecture (EDA) can build the basis for the implementation of a Real Time Enterpris-
es. The combination of these two architectures is also called SOA 2.0 [9]. In this con-
text CEP, Simple Event Processing and Event Stream Processing are software tech-
nologies for the implementation of Real Time Enterprises. Simple Event Processing
means that simple events directly trigger specific results. In contrast, Event Stream
Processing is a technology for processing continuous event streams.
Fig. 2. Software Technology and Architecture
2 Research Question and Goal
In order to adapt CEP for applications in manufacturing environments, further re-
search is necessary in the fields of knowledge representation, event modeling and
event processing.
The goal of the approach represented here is to develop a situational event man-
agement. This situational event management should be based on the CEP technology.
The focus lies on the development of a procedure for the deduction of rules for the
CEP engine. Currently, experts have to design and understand event models that are
representing the dependencies and relationships between event objects of the business
processes [10]. Moreover, they have to understand the historical data and derive
knowledge in order to implement declarative rules together with IT experts. Within
this work a method that maximally systematizes and automizes this proceeding should
be developed.
The main research questions are:
• Which events are available and relevant for the real-time control of complex pro-
duction processes?
• How can data of different sources be brought together in an event model?
• How can rules systematically and automatically be generated for event processing?
• Which actions are suitable for the situational event handling and control of the
production processes?
3 Proposed Approach
The solution components for the realization of the event handling consist of the fol-
lowing three areas:
• event modeling,
• event processing and
• event reaction.
These three solution components build on one another strongly and influence each
other. Since event processing is the central component of the event handling process,
it is the focus of this work
Event
Handling
Event Event Event
Modelling Processing Reaction
Expert
Data Mining Processes
Typ Bedingung Knowledge
Rules
Data Bases
Typ Typ Condition Action
A->B->C
New Events
Abstraction Level A==B
Services Monitoring
…
Constraint Constraint
Fig. 3. Approach for Event Handling
3.1 Event Modeling
In order to formulate the rules for the CEP engine an event model has to be described
to visualize the relations, constraint and abstraction levels. A modeling method for the
description of the differences between the relevant events and dependencies has to be
chosen. In addition, the relevant events for the real-time control of the production
processes should be identified and be compared with standards and norms in the field
of event definition. Moreover, alternative modeling methods and languages regarding
event modeling should be compared.
However, first assessments have shown that the Unified Modeling Language (UML)
is suitable for event modeling. Graphical event models and event constraints can be
described by UML.
3.2 Event Processing
For this purpose, the area of rule languages and thus the evaluation and selection of
possible software solutions has to be illuminated. The design of the rules should be
based on expert knowledge and methods such as data mining. Alternative control
languages and alternative event handling methods, like manual or automatic response
to events, have to be elucidated. Different software solutions have to be considered
for the realization of a CEP engine. The main focus of this part is the development of
a methodology for the creation and adaption of rules for the CEP engine.
The methodology will be based on two pillars. One is the graphical user interface for
domain experts and the other one is the establishment of rules by data-mining.
For the development of the rule system the requirements have to be collected, evalu-
ated and analyzed. Important requirements are usability of the GUI and the reliability,
flexibility and scalability of the system.
3.3 Event Reaction
Finally, the area of possible reactions to events has to be examined. The possible ac-
tions triggered by the event rules have to be defined and compared. Different output
media such as tablet PCs, headsets and higher level IT system are suitable.
The main question will be: is a manual or an automatic response to events more
appropriate for the field of production? Therefore, in an attempt to answer this ques-
tion, a scientific study will be carried out in the context of the use cases explained in
Chapter 4.
4 Use cases
The exemplary implementation of the method will be carried out by means of a
complex Cyber-Physical Production System (CPPS) at the Fraunhofer Research Insti-
tution for Casting, Composite and Processing Technology IGCV in Augsburg and
demonstrated using an example of a mass production for CFRP components.
The Learning factory for cyber-physical production systems (LVP) at Fraunhofer
IGCV in Augsburg shows a modern production presented by the example of a gear-
box.
The production scenario is based on the manufacturing and assembly of a gear box
with a high number of different variants. The different parts of the gear box are pro-
duced on several machine tools. Subsequently, there is a quality assurance step before
the variant specific parts are assembled on several stations. After this last step of the
production process, the gear boxes can be delivered to the customers.
The demonstrator consists of two material stocks, a turning machine as a produc-
tion resource, a quality assurance station and two manual assembly stations. In addi-
tion to these fixed resources, there is a mobile robot responsible for the material
transport.
The goal of the prototypical implementation is the realization of an intelligent pro-
duction control as a reaction to certain events during the manufacturing process of the
gear box. The handling of production information in smart products is realized by
RFID tags. Several RFID antennas have been installed throughout the demonstrator to
enable the reading and writing of information on important points during the produc-
tion process.
Moreover, the situational event handling will be demonstrated using an example of
a mass production for CFRP components. Within Carbon Fiber Reinforced Plastic
(CFRP) production processes many events affect the quality and production planning
and control. Through the interaction of the individual events, such as mold tempera-
ture, completion of required preforms, and so on, complex situations arise, which
should be monitored and analyzed in real time. CEP is suitable for this application.
Challenges for the mass production of CFRP components include the high costs
and the lack of transparency of the dependencies between the events that occur in the
production process. In order to reduce the inventory, the transparency along the sup-
ply chain has to be reduced and manual operations have to be eliminated. This should
lead to lower costs, increased responsiveness to changes during the production pro-
cess and reduced efforts for rework.
The process costs can be reduced up to 40 % by the further optimization of the pro-
cesses for the production of CFRP components. [11]
Fig. 4 CFRP Process and Events
5 Conclusion and future work
The use of the software technology "Complex Event Processing (CEP)" should be
examined in more detail for the production area. A near real-time monitoring and
control of production and logistics processes can be realized with the help of CEP.
For the present work this should be realized within a learning factory for digitalized
production (LVP). The LVP shows the practical perceptibility of “Industry 4.0” for a
richly varied gear production.
In addition, the feasibility will be shown for a real CFRP manufacturing process as
part of a research project. The event processing will be developed for this use case,
and thereby the procedure for developing a situational event handling will be vali-
dated.
6 Acknowledgements
The LVP is built up within the research project “Digitalized Production” as part of the
framework "BAVARIA DIGITAL". We extend our sincere thanks to the Bavarian
Ministry of Economic Affairs and Media, Energy and Technology for the funding of
our research.
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