Industry 4.0

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Industry 4.0
by Tekniker/ES and
Brockhaus Group

Who we are:
✔ A partnership between 2 companies:
➢ Tekniker / Basque country, Spain and
➢ Brockhaus Consulting
✔ Expertise in the field of:
➢ Software Development and Software Architecture
➢ MES Systems (also in automotive sector)
➢ Data Acquisition and Analysis in the field of predictive
maintenance

What does industry 4.0
mean to us
(and to our customers)

Industry 4.0
The 4th industrial revolution
The focus for machine building industry might/must shift from
hardware to information and information processing
… let’s say: software.
--- Prof. Dr. Detlef Zühlke / DFKI (Saarbrücken)

Industry 4.0
✔ Industrial Revolution 1.0
Beginning in the UK in the late 18th century, leads to machine
manufacture with the introduction of
mechanical production methods.
At the start of the 20th century, mass
industrial production in which
assembly line and factory principles
of products for mass consumption.
At the start of the 1970s, the deployment of
electronics and IT in industrial processes led
to optimized and automated production.

Industry 4.0
Continuation of the automation of production processes, human
responsibilities being overtaken by machines.
Over the next decade the fourth industrial
revolution will usher in an age of
"decentralized" production.
Industry 4.0 is made possible by smart objects,
autonomous products and decision-making
processes which manage manufacturing and
production networks in close-to-real-time.

Industry 4.0
Value proposition of industry 4.0
✔ Increase agility and flexibility in production process.
✔ Individualize smallest volumes to a high degree of productivity.
✔ Optimizes multi-variant series production.
✔ React faster on changing market requirements.

Industry 4.0
What means industry 4.0 to us?
✔ Data is important to increase production quality, storage
capabilities improve drastically with NoSQL databases.
✔ New technologies provide improved control over your production
processes; e.g. optimize maintenances times
/ down times by prognosis of upcoming failure
of equipment.
✔ New technologies allow different way to
interact, e.g. mobile devices for operating,
supervising and collaboration
✔ New standards like OPC UA and OPC / Java
allow to close technological gaps between shop
floor and business-oriented applications

The current approach

Industry 4.0
✔ Operate equipment by means of mobile devices
(with respect to actual position of operator)

Industry 4.0
✔ Supervise equipment by means of OPC
integration and interaction through SCADA
system on mobile devices

Industry 4.0
✔ Visualize additional data (KPI, technical
documentation, …) of equipment by means
of QR code and devices like google Glass
(or similar)
✔ currently under development!

Industry 4.0
✔ Analyse sensor data for purposes like
predictive maintenance, trends and
quality reports
✔ There is a lot of information in production plants:
➢ From several sensors
already installed
➢ From the control parameters
➢ Right now a lot of that
information is not used,
just a small part is used.
✔ currently under development using NoSQL / Big Data
databases and statistical frameworks like weka or Apache Mahout /
Spark

Reference projects

Windmill Park
3. ANOMALY
PREDICTION AND
DETECTION
2. MODELLING
e.g. temperature
1. CLUSTERING, e.x.
based on wind turbine
performance
Groups of wind
turbines with similar
performance
Temperature modeling and prediction based on SCADA
dataset, under normal operating conditions.
 e.g. Generator/Gearbox: bearings temperature
Wind farm anomaly detection based on those
temperatures related to the failure:
 e.g: bearing temperature monitoring
Too high % of deviation on
temperature X regarding baseline (the
farm, the model). Dangerous for the
Generator performance.
Client: INGETEAM
Analyzing current sensor values for modeling and predictive maintenance

Machine tool
Models
Nominal
conditions
Failure A
Failure B
…
Vector
Clustering
Feature
selection
Frequency Domain:
- Power bandwith
- Spectral lines
Time domain
• RMS
• Curtosis
• …
…
Machine2Fingerprint
Machine1Fingerprint
…
MachineNFingerprint
Currentand
Power
Monitoring
Othersensors
monitoring
Pattern recognition
Prediction/
Prognosis
Realtime
monitoring
Model based analysis Automatic learning
Motorize internal signal (like electric consumption) for predict fails in heads and
lineal guides.

17
MES System for Automotive Supplier
Architectural lead on ISA 95 compliant MES System realized with Java EE
The existing solution came to an end in
terms of maintainability and technologies.
The customer asked for a modern solution
catering all needs.
• Training internal teams
• Defining the a new architecture
• Implementation lead
• Staffing of development teams
Implemented for:
• Honda / Ohio
• Volkswagen / Wolfsburg
• Ford / China, Thailand, Turkey and India
Consulting/ Development
Training / Coaching
Research / Academia



18
MES System for Film-stretching industry
Architectural lead on ISA 95 compliant MES System realized with Java EE and Big Data
The existing solution was build by different
vendors and not according to modern
requirements.
The new solution should be re-build based
upon standards like ISA 95 and latest
technologies.
• Training of internal teams
• Defining the a new architecture
• Implementation lead of
distributed teams in
Germany and Romania
• Staffing of development
teams
Training / Coaching
Research / Academia



19
Software Architecture: Review
Architectural review of the application related to planning the production of
the powertrain components of a car manufacturer in Germany
The existing solution was older than 16 years,
maintenance and improvement were almost
impossible due to technical debts.
• Evaluating the existing architecture
• Identification of problematic hot spots
• Defining an optimization strategy
• Developing the new reference architecture
Training / Coaching
Research / Academia



20
eConvey: Operating and supervising conveyors
Implementing a new, mobile solution for operating and supervising
conveyors in the automotive sector (mass production)
The existing solution was operated by 250+
touch screens, the mobile solution was
developed to replace several of these.
Based upon direct OPC interaction the
solution allows:
• to operate the conveyors
(< 5 ms latency),
• to localize the operator (Bluetooth LE),
• to vizualize the current state of the
conveyor(s) by means of a VISU
• to retrieve additional information
by QR-Codes
Training / Coaching
Research / Academia



21
Capturing and analyzing production data
Production data and Big Data Storages in production lines
within the automotive industries
The master thesis of Steffen Templin was done
in collaboration of Dürr Systems, University of
Siegen and Brockhaus Group.
• Real-time
Training / Coaching
Research / Academia



22
Master Thesis: Industry 4.0, CEP and Big Data
Joint Master Thesis in the field of Complex Event Processing and Big Data Storages
in production lines within the automotive industries
The master thesis of Steffen Templin was done
in collaboration of Dürr Systems, University of
Siegen and Brockhaus Group.
• Real-time analysis of complex events from
several streams / sensors in production
processes
• Storing large amounts of sensor data in
NoSQL datastores
• Prototypical implementation using
OpenSource technologies (WSO2 CEP /
ESB, MongoDB)
Training / Coaching
Research / Academia



23
Persons in charge
Prof. Dr. Ing. Thomas Barth
- Studiengangleiter RFH Köln -
Prof. Dr. Julian Reichwald
- Schwerpunkt Industrie 4.0 DHBW Mannheim -
Miguel Perez
- Projektleitung Industrie 4.0 -
Enrique Plaza Soria
- Big Data und Machine Learning -
Matthias Bohnen
- Geschäftsführung Brockhaus Gruppe -

24
Enough propaganda ...
Contact information:
Brockhaus Consulting GmbH
Gustav Stresemann Ring 1
D - 65189 Wiesbaden
Germany
Fon: +49-611-97774-332
Fax: +49-611-97774-432
Web: www.brockhaus-gruppe.de
Mail: office@brockhaus-gruppe.de

Industry 4.0

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