Research and Teaching at IAS

Institute of Industrial Automation and Software

Engineering

Research andTeaching at IAS

2021

Prof. Dr.-Ing.

Dr. h. c.

Michael Weyrich

2021University of Stuttgart, IAS, Prof. Dr.-Ing. Dr. h. c. Michael Weyrich 2

Faculty of Computer Science, Electrical Engineering and Information Technology of the

University of Stuttgart

Institute of Industrial Automation and Software Engineering (IAS)

We see ourselves as a bridgehead to Product and Plant

Automation in the research disciplines of Information Technology,

Software Technology and Electronics.

Prof Weyrich was appointed to the University of

Stuttgart in April 2013.

Research and teaching at the Institute focuses on the topic of Software Systems for

Automation Engineering.


1935 – 1970

Institute for Electrical

Plants

Professor A. Leonhard

1970 – 1995

Institute for Control

Engineering and Process

Automation

Professor R. Lauber

1995 – 2015

Institute for Automation

and Software

Engineering

Professor P. Göhner

seit 2013

Professor M. Weyrich

Institute of Industrial

Automation and Software

Engineering

85 years of Tradition and Progress

Institute of Industrial Automation and Software Engineering (IAS)

Faculty of Computer Science, Electrical Engineering and Information Technology of the


17x P

hD

stu

dents

3x

Managm

ent

Prof. Weyrich Dr. Jazdi Jun.-Prof. (TT) Morozov

6x

Serv

ice

30 –

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The institute conducts about 1000 exams per year.

Teaching

Study programs

- Electrical Engineering department:

• B. Sc. & M. Sc. Elektrotechnik und Informationstechnik

• B. Sc. Erneuerbare Energien

• M. Sc. Nachhaltige Elektrische Energieversorgung,

• M. Sc. Elektromobilität

• M. Sc. Information Technology

- Exports to other departments

• Mechatronik, Technische Kybernetik, Informatik, Medizin-

technik, Technikpädagogik, Verkehrsingenieurwesen

- Interdisciplinary

• M. Sc. Autonome Systeme (Dean of Studies Office)

Lectures

Industrial Automation I & II

Technologies and Methodologies of Software Systems I & II

Software Engineering for Real-Time Systems

Industrial Automation Systems

Basics of Software Systems

Lecture Series: Software and Automation

Lecture Series: Aspects of Autonomous Systems

Reliability of intelligent distributed Automation Systems

Networked automation systems (planned WS21/22)

Seminar Intelligent Cyber-Physical Systems

Laboratory Course Software Engineering

Laboratory Course Industrial Automation

Laboratory Introduction in Microcontroller Programming


We focus on automation systems, especially their software in connection with control

systems.

Research at IAS

• 5G based Intelligent Digital Twin

• Multidimensional Synchronization of Digital

Twins

• Co-simulation of software-defined systems

• Automatic reconfiguration management

• Artificial intelligence and dynamic reliability

• Decentralized, cooperative machine learning

in automation

• Dynamic reliability calculation

• Test of distributed components

• Safeguarding of autonomous systems

Complexity

control in

automation

technology

Intelligent

Automation and

Autonomous

Systems

Safeguarding of

automation

systems and

components

Research topics at the IAS

• 5G-based Intelligent Digital Twin

• Co-simulation of software-defined

automated systems

• Autonomous reconfiguration

management of software-defined

systems

• Multidimensional synchronization of

digital twins for different applications


How can the complexity of cyber-physical systems be made manageable in engineering

and operation?

Research Area: Complexity control in automation technology


• Dynamic reliability calculation

• Testing of distributed components

• Verification and validation of

software updates

• Safeguarding of autonomous

systems

• Cognitive sensor networks in safety-

relevant systems


How can we rely on the quality of automated systems in terms of reliability, safety and

availability?

Research Area: Safeguarding of automation systems and components


• Dynamic Intelligent Reliability

• Optimization of automation systems

using machine learning

• Intelligent automation for user-oriented

• Soft sensors for networked automation

architectures

• Decentralized, cooperative machine

learning in automation

• Simulation of autonomy concepts


Are technical systems of tomorrow going to automate themselves?

Research Area: Intelligent Automation and Autonomous Systems


The Institute follows the mission statement "Intelligent Systems for a Sustainable

Society" and is part of the Excellence Strategy of the University of Stuttgart.

IAS in the Research Environment of Stuttgart

Institute of Industrial Automation

and Software Engineering

We are part of the profile areas and

emerging fields of the excellence strategy:

• Autonomous systems

• Architecture and adaptive building

• Production technologies

• BMWi flagship initiative: SofDCar

• BMBF flagship initiative: H2Mare

Research Factory

Intelligent Systems

Technology transfer

https://cyber-valley.de/

Industry 4.0

Assembly plant

Data Analytics in

Manufacturing

TestIAS with

Virtual Reality

Intelligent Test

of autonomous

systems

Inte

llig

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uto

ma

tio

n &

Au

ton

om

ou

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ys

tem

s

Co

mp

lex

ity c

on

tro

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PS

Re

lia

bil

ity o

f A

uto

ma

tio

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ys

tem

s

Simulation of

plant

modernization

Digital Truck

Twin

Plug & Simulate Blockchain

Application

scenarios

Intelligent

Warehouse

(ARENA 2036)


The model processes are used to represent special automation technology and to

demonstrate the capabilities of software systems.

Model Processes at IAS

Context aware pill

dispenser

Real-Time

Locating System

Scenario-based

testing of

autonomous

driving functions

Digital twin of a

modular

production

system


Cooperation with the following Companies

CompWare Medical GmbH

Daimler AG

Diffblue Ltd.

Festo AG & Co. KG

Hirschvogel Umformtechnik GmbH

iss (Innovative Software Services GmbH)

OTTO FUCHS KG

Robert Bosch GmbH

Siemens AG

SMS group GmbH

Vector Consulting GmbH

Vector Informatik GmbH

Infineon Technologies AG

ZF Friedrichshafen AG

ETAS GmbH

Nokia Corporation

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V.

Validation and verification of

highly automated and

autonomous systems

Indoor

Navigation

Systems

Simulation and commissioning

of robots in virtual reality

Create technologies that

combine power generation with

efficient control systems.


IAS supports various start-up companies and cooperates in research projects

Maker Space

since 2021 VC (geplant)

Jan. 2017 – Dez. 2017 EXIST

Aug. 2019 – Juli 2022 EUREKA-Projekt

Apr. 2014 – März 2015 EXIST

März 2016 – Feb. 2018 Junge Innovatoren

Juni 2014 – Mai 2015 EXIST

Juni 2015 – Mai 2016 Junge Innovatoren

RoboTest

deaptwin

Machine parts detection

application for higher product

quality

since 2021 EXIST (geplant)

Prof. Weyrich is also the faculty's start-up officer and thus the first point of contact for those

interested in starting a business.

•Research Activeties


Co-Simulation (Cooperation with Vector Informatik)

Dynamic Co-Simulation of heterogeneous Internet of Things Systems


Requirements:

„Plug-and-Simulate“-capable Co-Simulation of

heterogeneous and dynamically changing IoT

systems

Core technologies:

Agent-based Co-Simulation

Component- and Process modeling with MATLAB,

AnyLogic, Unity, OMNet++, …

Approach

Framework for coupling simulations via an agent

system

Connection of the simulations via interface

adapters

Service-oriented modeling of communication and

physical processes

Synchronization of the partial simulations via a

central clock agent

Agent

Agent

Agent

AnyLogic

MATLAB

OMNet++

Unity

How can

different

simulations be

coupled at

runtime?

𝑋

𝑈

( 𝑢1, 𝑥1 , 𝑦1)

( 𝑢2, 𝑥2 , 𝑦2)

Process 1

Process Chain

Process 3

Process 2

𝑢(𝑠)𝐺(𝑠)

𝑞(𝑠)

𝑒(𝑠)

𝐺(𝑠)

Component-related discretization

and storage of all time series

𝑌

Training the

network on

historical data

1 2 Using the

trained

model for

more robust

intervention

Methodology


© 2017 IAS, Univ. Stuttgart

Requirements:

Analysis of process data for compliance with

defined quality characteristics

Real-time recommendations to the worker

Core technologies:

PLC-based data acquisition

Feature extraction

Data Analytics (online/offline)

Motivation

Sensor data contains information about the plant

and process status and can be used to improve the

process quality

Approach

Systematic extraction of unknown relationships and

patterns

Data acquisition and integration, dimension

reduction, data analysis, recommendations

Data-driven quality optimization

Adaptable quality controlControl of discrete manufacturing machines based on Long Short-Term Memory networks

How can we

improve the

product quality

by a learning

control?

Approach

Planning of an industrial automation system is

modelled as a dialog-based process and applied

to an agent system

Agents represent resources and try to integrate

them into the planned automation system

Determination of possible constellations for the

automation system to be planned


Requirements:

Support of the planner in the rough planning

phase of industrial automation systems

Core technologies:

Agent technology (Self-organisation)

Metaheuristics (layout optimization)

Self-organized reconfiguration managementDecentralized, self-organized planning of automation systems

ID 577

ID 577

How can we

achieve autonomous

reconfiguration

management?



Requirements:

Analyze decisions in terms of their risk at runtime.

Evaluate outcome and basis for decision-making

(models)

Core technologies:

Digital Twin

Automatic rule extraction

Data Analytics

Safeguarding of autonomous systems in operationinterpretable safety assessment of dynamic decision processes at runtime

How can the risk

of a decision be

quantified at

runtime?

Is my

route

safe?

Motivation

Avoid critical situations proactively

Operational data contains information about the

uncertainties inherent in the models.

Approach

Systematic extraction of unknown influences through

hypothesis generation and their testing.

Trade-off between safety and efficiency based on

estimated uncertainty in planning

Data-driven optimization of the safety margin

New



Requirements:

Automated increase of test case coverage of

trained models

Generated test cases are relevant and

challenging for the trained model

Core technologies:

Machine Learning

Parameter dependent environment simulations

Numerical optimization methods

Motivation

Human experts can poorly assess which concrete

test cases are challenging for a machine-trained

model

Approach

Parameterized environment simulation of a typical

deployment scenario

Iterative optimization of simulation parameters to

generate challenging test cases

Automated test case generation for machine-trained modelsGeneration of new challenging test cases by optimization methods

How can

machine-trained

models be

effectively

tested?

New


Context-aware cyber-physical systems using the example of adaptive user support in the health sector


Approach

Acquisition of heterogeneous and dynamic data for

context modeling (environment parameters, user

data, etc.)

Middleware-based architecture for scalable reuse

of the context model by applications.

Use case: development of a tablet dispenser as an

IoT approach with context-adaptive user support

Requirements:

Acquisition, modeling, and analysis of

heterogeneous data around a cyber-physical

system to derive applicable knowledge.

Core technologies:

Context-aware automation systems

Middleware-based architecture

Graph algorithms for consistency checking of

time-based context models

How can pill-taking be

supported in a context-

sensitive way?Mobiler und stationärer Tablettendispenser

Adaptive Assistenz

Heterogene und

dynamische Daten

Kontext Middleware

Intelligente Kontextgenerierung

Kontext-Modellierung

Kontext-Reasoning

Kontextverwaltung

Datenschicht

Anwendungsschicht

Datenerfassung- und vorbereitung

Adaptive AlarmierungEmpfehlungen zur Adhärenz -

Erhöhung

Alarmeinstellung

Umgebungsdaten

Gesundheitsdaten

Profildaten

Einnahmezeiten

Medikationsplan

New


Requirements:

Handling of input data (usually time series data) of

different dimensions

Solution of regression and classification problems

Core technologies:

Two-stage deep neural network algorithm

Representation database for storing and

exchanging characteristic feature sets

Client-to-client communication architecture

Motivation

Efficient learning despite

data sets that are often small in everyday

industrial life

dynamic processes that require continuous

updates of the learning model.

Approach

Transfer of knowledge between algorithms

that are able to learn

Deep Industrial Transfer LearningLearning automation systems with dynamic knowledge transfer

New

AI for intelligent testing of autonomous systemsValidation and verification of autonomous systems and their components


Requirements:

Manufacturers announce concepts for

autonomous systems, but tools for verification

and validation are needed

Core technologies:

Computational Intelligence

Probabilistic methods (e.g. Bayesian networks)

Approach

Test cases are grouped with AI

Deep Rule learning for transparent rules

Prioritization of test cases

How can

autonomous

systems be

tested

intelligently?



Requirements:

Complexity control in variant management

Assistance for the system engineer

Core technologies:

Feature Oriented Domain Analysis

150%-Modell

Data Analytics

Motivation

As systems become more complex, the solution

space for system development becomes

unmanageable

Approach

Model repository for known relationships and

patterns

Use of an appropriate methodology for variant

management

Model and data based variant generation

Automated variant managementGeneration of system architectures in systems engineering

New

How to create

approximately all

variants of a

system and select

one?


Assistance of modernization in industrial automationHow can we support the modernization of old plants?


Approach

Modeling of the machine with little effort, software

agents represent components of the machine

Automated evaluation of production requests

Generation and evaluation of adaptation options

using the model of the machine

Decision support and assistance in the

modernization planning process

Requirements:

Software tool which supports the modernization

process of machines or plants

Generation and evaluation of adaptation options

based on given requirements

Core technologies:

Product-, process-, resource-model of the

machine

Agent-based assistance system

How can we

support the

modernization of

old plants?



Requirements:

Integration and uniform semantic description of

heterogeneous data

Generation of knowledge for the application from

unstructured and heterogeneous data

Core technologies:

Ontologies

AI methods (ensemble learning, feature extraction)

Graph Analytics

Motivation

Use of existing data diversity from different, dynamic

sources to generate knowledge for applications

Approach

Intelligent data integration from heterogeneous

sources

Connecting heterogeneous data, artificial

intelligence as well as analytical models

AI-based knowledge generation

Robust learning based on heterogeneous dataKnowledge generation in automation technology using data diversity

How can

knowledge be

generated from

unstructured,

heterogeneous

data?

Künstliche

Intelligenz

Anwendungen

Datenbank

Prozessdaten

Unstrukturierte

Dateien

Nutzer

Analytische

Modelle

New

e-mail

web

phone +49 (0) 711 685-

fax +49 (0) 711 685-


Thank you!

Pfaffenwaldring 47

70550 Stuttgart

Prof. Dr.-Ing. Dr. h. c. Michael Weyrich

67301

67302

Institut für Automatisierungstechnik und Softwaresysteme

[email protected]

Institute of Industrial Automation

and Software Engineering

www.ias.uni-stuttgart.de

Documents

Research and Teaching at IAS