4422_WP_Smart_Engineering_EN_web__S.1-x

WHITE PAPER
SMART ENGINEERING
WITH BIG DATA AND
DIGITAL TWINS
How big data signal processing, automated situation recognition and
digital twins are changing the engineering discipline

CONTENTS
INTRODUCTION 4
NEW IMPACTS TO ENGINEERS 6
NEW METHODS OF DATA HANDLING SPEED UP DEVELOPMENT 8
MORE EFFICIENT MEASUREMENT DATA MANAGEMENT
WITH BIG DATA METHODS 10
DIGITAL TWINS 12
ADDED VALUE VIA SCENARIO RECOGNITION,
CREATION AND SIMULATION 14
WHO WILL WIN THE RACE? 16
T-Systems International GmbH
Hahnstraße 43d
D-60528 Frankfurt am Main
Authors:
Wolfgang Holz
Dr. Christoph G. Jung
Sascha Leidig
Bastian Wymar
Organisation:
Project manager: Christopher Link
Layout: Peter Brücker/Norman Mascher-Aspensjö

INTRODUCTION
Digitization is changing the world. Technology, innovations and especially
ever-growing data ecosystems are turning the industrial economy on its
head. Markets have long began to adapt to the changing environment
and are remodeling business concepts and rethinking value chains. Data
is the new oil, and an intelligent exploitation of data within industrial processes
and products has become a key success factor for generating new
revenue and optimizing the way value is created.
Manufacturing companies, for example vehicle manufacturers, have a
keen interest in data that provides insights about their products, in this
case vehicle usage, functionalities of digital assistants or quality. Such
data can provide knowledge, that is valuable for developing new services
like autonomous driving. Customer interaction can be improved too, for
example by being able to address drivers in particular situations, such as
when the first indicators for serious wear appear.
Collecting and intelligently analyzing data forms the fundament for all
these activities. In this context, more and more so-called digital twins are
being used, i.e. digital copies of physical objects. These may be vehicle
components or the vehicle as a whole. In vehicle development digital
twins offer particular potential for optimization, for they enable developments
to be largely tested on purely digital simulations. This, in turn,
offers major potential savings in terms of time and money.
Up to now, vehicle parts and control software have been developed, then
tested on many thousands of kilometers of test tracks in order to get
results that can trigger improvements. This process takes several months
and, depending on the outcome of the testing, has to be repeated more
than once until the required vehicle properties and quality requirements
areachieved. Now digital twins can simulate vehicle components in their
real-life contexts, which means they no longer need to be physically produced
or tested on test tracks. Rather, existing test data can be used on
digitally simulated components and thereafter be evaluated. As a result,
engineers get results far more quickly, but at the same time they face
challenges linked to the use of new, digital technologies such as big data
analysis and predictions by artificial intelligence.
The main challenges are the provisioning of suitable hardware and sensors,
the efficient management and processing of gigantic volumes of data and
the development of intelligent analysis algorithms. In this way data can be
converted into insights and then again into design improvements.
The prerequisites for this new type of engineering have been put in
place by the technical innovations of the recent years, for example the
ubiquitous networking, the availability of less expensive hardware and
sensors, and the constantly improving capabilities of data storage and
data processing.
This white paper is aimed at engineers, developers and IT managers who
also face digital engineering challenges. It provides a clear answer to the
question of how big data technologies can be used to improve development
processes in the control software area.
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NEW PARAMETERS FOR ENGINEERS
NEW IMPACTS TO ENGINEERS
One of the really hot topics in the automotive industry is autonomous driving. Visionary
pioneers like Elon Musk as well as German industry experts talk about a revolution in industry
and society. This means that autonomous driving will not only fundamentally change the
driving experience, it will also change the car as an object to be purchased and used. It will
increasingly turn driving into a mobility service in the personal and commercial transport
sectors. In the sense of the “shared economy”, autonomous driving will make it far easier to
make the car available – autonomously – to other road users as a transport service during its
“downtime”, i.e. when its user is not using it.
However, to enable this type of usage scenario, it is vital that unmanned driving is made as
safe as possible. So the engineering input and software developments along that path occur in
a tense area involving on-board electronics and connectivity, software and smart algorithms in
the vehicle, and communication links between vehicles and an adequate IT infrastructure. This
infrastructure includes smart systems that communicate with vehicles and, for example, offer
and manage services.
As a result, vehicle electronics, on-board sensors and vehicle bus systems are becoming
increasingly complex, while driving functions run autonomously in the car and have to prove
themselves in traffic situations. So, for development, it is important that industry expertise
and digital technology expertise coalesce, i.e. that development teams incorporate digital
engineers working on big data architectures, signal data processing, data management in
data lakes and data analytics. And, secondly, the teams also need to incorporate automotive
engineers working on interpreting data and deriving conclusions as to what the data obtained
says about, for example, the quality of driving functions and driving behaviour, so that control
software can be modified.
In this segment the time-to-market is shrinking rapidly, and the battle to become the first automotive
manufacturer to be able to offer safe autonomous driving has long begun.
Execution speed for continuous software developments, implementation as well as operations,
have now also reached the automotive industry, and it will be a key success factor in the industry’s
future.
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