WHITE PAPER

SMART ENGINEERING WITH BIG DATA AND

DIGITAL TWINS How big data signal processing, automatic situation recognition and

digital twins are changing engineering

T-Systems International GmbH Hahnstraße 43d D-60528 Frankfurt am Main

Authors: Wolfgang HolzDr. Christoph G. JungSascha LeidigBastian Wymar

Organisation:Project manager: Christopher Link Layout: Peter Brücker/Norman Mascher-Aspensjö

CONTENTS

INTRODUCTION 4

NEW PARAMETERS FOR 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 IS LEADING THE RACE? 16

INTRODUCTION

Digitisation is changing the world. Particularly in commerce where, in al-most every industry, technical innovations and ever-growing data ecosys-tems are changing business models and value-added chains. It is a case of “data is the new oil”, and the uses of data in industrial processes and products are generating huge potential for optimisation and brand new revenue sources.

So manufacturing companies, for example vehicle manufacturers, are extremely interested in data that provide insights into their product, in this case vehicle usage, vehicle properties and quality. Such data can provide knowledge which is valuable in terms of developing autonomous driving. Customer interaction can be improved too, for example being able to address drivers in particular situations, for instance before signs of wear appear.

Collecting and intelligently analysing data forms the basis 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 com-ponents or the vehicle as a whole. In vehicle development digital twins offer particular potential for optimisation, for they enable developments to be tested on digitally simulated components. 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 kilometres of test track in order to get results that can drive 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 are

achieved. Now digital twins can simulate vehicle components in driving situation contexts, which means they no longer need to be physically pro-duced or tested on test tracks. Rather, existing test data can be used on digitally simulated components and 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 and artificial intelligence.

The main challenges are providing suitable hardware and sensors, effi-ciently managing and processing gigantic volumes of data and develop-ing intelligent analysis algorithms. In this way data might be converted into insights and then again into design improvements.

The prerequisites for this new type of development have been put in place by the technical innovations in 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 develop-ment processes in the control software area.

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NEW PARAMETERS FOR ENGINEERS

One of the really hot topics in the auto industry is autonomous driving. Visionary pioneers like Elon Musk and German industry experts talk about a revolution in industry and society. This means that autonomous driving will not only fundamentally alter the driving experience, it will also change the car as an object to be purchased and used. It will increasingly make driving a service in the personal and goods transport sectors. In the sense of the “shared economy”, autonomous driving will make it far easier to make the car available – autono-mously – 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 testing traffic situations. So, for development, it is important that sector exper-tise and digital technology expertise coalesce, i.e. that development teams have, firstly, digital engineers working on big data architectures, signal data processing, data management in data lakes and data analytics. And, secondly, 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 market segment the speed of development is increasing rapidly, and the battle to become the first auto manufacturer to be able to offer safe autonomous driving began some time ago.

The speed with which continuous software developments are being implemented, as well as the software made available and operated, has now also reached the auto industry, and it will be a key success factor in the industry's future.

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NEW PARAMETERS FOR ENGINEERS

NEW METHODS OF DATA HANDLING SPEED UP DEVELOPMENT

“Intelligent measurement data analysis and synthesis” is a key tool when processing and get-ting on top of the immense volume of data in vehicles.

Modern big data methods such as this one have now learned to handle the vast amounts of data recorded in vehicles and associated backend log files so efficiently and flexibly that an increasing degree of automation can be achieved when evaluating test data and test bench experiments. In this context we talk of event recognition, or the automated detection of driving situations, for example critical emergency braking with ABS intervention, or overtaking ma-noeuvres carried out in the rain on a motorway.

Fig. 1: Measurement data analysis and simulation with big data signal processing and digital twins

Semantic integration and machine learning can also be used to enrich such events in driving situations with metadata from design, production and after-sales, so that one may draw analo-gous conclusions for previously untested scenarios and with combinations of events that are critical because they have not been satisfactorily resolved. Ultimately, simulation environments can be systematically generated in this way from recorded raw signals and synthetic event models. Nowadays these environments do not merely involve physical, driving physics simula-tions and individual driving components – they also enable the instrumentation of mock-ups of the associated vehicle backends, for example to study the failure of coupled geo-services in the newly developed software version of a networked control unit, without actually having to put the unit on the road or the test bench. This generates a time saving, without which future developments cannot be achieved on time. These advanced HIL/SIL (Hardware-/Software-in-the-Loop) solutions also help to cut down on actual test drives (environmentally friendly) while improving test coverage and thus safety and quality scores.

Fig. 2: Simulation environment for driving physics

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NEW METHODS OF DATA HANDLING SPEED UP DEVELOPMENT

Scenario creation

Semantic search

Simulated mock backend

VR simulation

Online vehicle backend

Events

Pattern recognition

Telematics

Playback

Big data

signal processing

Engineering Production After-sales

SW status x

SW status 1

Semantic integration/assignment

Test vehicle

Digital twin

Assessment

Transcode measurement data Record master data/transactions

MORE EFFICIENT MEASUREMENT DATA MANAGEMENT WITH BIG DATA METHODS

In the area of measurement data management with big data there was, for a long time, the problem that the prevalent recording formats, for example ASAM MDF1 and ATFX2, are not suitable for processing data in parallel. In particular, when recording different message types and frequencies, the format and coding change of samples (sample = a snapshot of the sig-nals from many associated sensors or device statuses) presented major problems for big data frameworks, such as Hadoop3, in terms of technically partitioning the files into segments that can be interpreted as independently as possible from one another.

But now there are some highly promising transcoding approaches, such as Norcom Dasense4 and Big Data Signal Processing (BDSP)5 from T-Systems International GmbH, which can use cluster and cloud technologies to break into these binary data containers and convert them to scalable formats like ORC6 and Parquet7.

1 https://www.asam.net/standards/detail/mdf/2 https://www.asam.net/standards/detail/ods/wiki/3 https://hadoop.apache.org4 https://www.norcom.de/dasense5 https://www.t-systems.com/de/best-practice/02-2018/fokus/datenernte/big-data-signal-processing-8060526 https://orc.apache.org/7 https://parquet.apache.org/

For example, by doing away with sample-based displays and minimising signal redundancies, BDSP can reduce the original recordings to up to 10 per cent of their original size, depending on the measurement channel. At the same time, this type of approach also brings with it the right calculation rules so that, for example, typical time series analyses can be run in these big data formats with as many computers as possible in parallel, which generates a substantial time-saving effect. As a result, typical engineering problems such as detecting outliers (actual signals, for example with a torque value, that periodically move outside the boundaries of the target torque value indicated by the control unit), counting emergency breaking procedures (as a derivation from the driving speed signal and the ABS status) or, for example, the non-avail-ability of a lateral stability control function, can be answered within a few minutes rather than after several days on standard servers using big data methods.

Finally, too, there is the issue of standardised, but specifically interactive, access to the meas-urement data analyses. Here, too, the big data approaches that traditionally come from batch processing have had difficulties in the past. In this context, T-Systems has opted for a REST8 gateway which can transfer, in a way that is safe from attack, pieces of code from a central point to the cluster's data-holding compute nodes. As a result, the engineer's analysis queries move towards the measurement data (code to data), rather than having to cost-intensively bring large volumes of raw samples to the engineer's desk (data to code).

This type of standardised gateway, which makes its calculated extracts (here: events/labels in the form of abstract signals and suitable extracts from the raw signals) available in all sorts of output formats, for example CSV, XML and JSON, as well as in the recording formats like MDF and ATFX which engineers and their measurement data tools are used to, also now constitutes the bridge to semantic methods when integrating all types of corporate data sources.

8 https://de.wikipedia.org/wiki/Representational_State_Transfer

Fig. 1: See P. 8

Fig. 3: Transcoding and analysis of measurement data with big data methods

Simulation file

MDF4, DAT, CSVDistributed big

data format

ORC/PARQUET

Results extract

REST + JSON, CSV, MF4, DATTrace file

ASC, ATFX, ADTF, VPCAP

Big data signal processing inscalable computer cluster

Transcoder Analysis

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MORE EFFICIENT MEASUREMENT DATA MANAGEMENT WITH BIG DATA METHODS

DIGITAL TWINS

Semantic integration and search is how T-Systems refers to the creation of “digital twins”, i.e. virtual copies of real vehicles and their components using the draft documents (these include, for example, the so-called on-board network catalogue of all the installed communication bus-es and their message types, the vehicle's exploded parts lists and the software and hardware statuses of the components involved) as well as their production and life-cycle history (assem-bly and paint processes, repairs, modifications). High-level Product Lifecycle Management (PLM) systems such as PTC Windchill9 are available for this purpose.

However, when it comes to the complex assignment problems involved in this, conventional databases have proven to be unsatisfactory. In their place, there are document and graph-based databases to display and model the hierarchical relationships and the flexible attribution (descriptive text, photo, CAD/vector graphic) in the entities (for example devices, module, channel, sub-parts list...) represented. Instead of traditional SQL interfaces, REST-based meth-ods10, 11, 12 based on the “Google“ search principle are being used here, too. For example, the search results or hitlists are scored by relevance, but extracts from relations graphs with entity nodes (semantic networks) are returned instead of hyperlinks to websites.

However, developing the relevant semantics (ontologies) and assignment logics requires extensive high-level experience with the domains to be integrated and with technical systems. Examples we should mention here are the PDM WebConnector13 from T-Systems International GmbH and the SemaSuite14 from T-Systems Multimedia Solutions GmbH, which bundle this experience in the form of ready-made blueprints for the automotive industry.

The abstract results from the measurement data transformation with big data methods are just one data source, albeit a very important one. Consequently, in the end result, all the key background items of information, such as the hardware and software components involved in a critical overtaking procedure, can be related to one other and researched.

9 https://www.ptc.com/de/products/plm/plm-products/windchill10 https://de.wikipedia.org/wiki/Resource_Description_Framework11 https://en.wikipedia.org/wiki/SPARQL12 https://graphql.org13 https://plm.t-systems-service.com/de/plm-produkte/integration-und-migration/soa-pdm-erp-integration/pdm-webconnector-71101214 https://semasuite.t-systems-mms.com/home.html

Fig. 4: Digital twin – semantic integration of corporate data sources

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DIGITAL TWINS

ADDED VALUE VIA SCENARIO RECOGNITION, CREATION AND SIMULATION

Based on large-scale semantic event networks, T-Systems are currently initiating some prelim-inary projects using pattern recognition and machine learning in the evaluated actual scenar-ios. Dimensionality reduction15 and suitable clustering16 methods can be used to examine the many document and attribute trees (here: the interplay of hardware and software components in a particular driving situation or, for example, the communication pattern of a vehicle with its backend) for, in particular, differences, frequencies and gaps. For it is the gaps, in particular, which provide pointers to, for example, less tested scenarios which need to be studied within a test plan.

Fig. 5: T-Systems project – dimensionality reduction and scenario creation based on communication patterns in vehicle-backend communication

By drawing analogies with existing, similar test recordings, a control unit's individual envi-ronment channels can now be extracted from big data signal data processing and suitable prototype and driving simulations such as PTC Creo17 and nVidia Drive Constellation18 and, along with artificial signals derived from physical models, be simultaneously consolidated on many computers into a single “virtual” recording file (in the usual sample and message-based formats). Now, to a certain degree, this file is used as playback for the relevant HIL simulators, for example for the CAN bus19 20 21, which acts almost like a karaoke machine for the control unit to be tested and for its software development.

15 https://en.wikipedia.org/wiki/Dimensionality_reduction16 https://de.wikipedia.org/wiki/DBSCAN17 https://www.ptc.com/de/products/cad/creo/simulate18 https://www.nvidia.com/en-us/self-driving-cars/drive-constellation/19 https://www.mastercan.com/mastercan_tool_en/20 https://www.dspace.com/en/pub/home/applicationfields/our_solutions_for/bussimulation.cfm21 https://www.adas-iit.com/hardware-in-the-loop/expertise/adas-hil-test-in-real-time/

Fig. 6: Hardware in the loop

At the same time, the requisite failure and communication scenarios can be configured in virtual vehicle backends, known as mock-ups. Their development environments are primarily based on modern container technologies, such as Docker22, so that, if necessary, a complete network, operating system and software setup can be duplicated in a cloud and manipulated as required.

The control unit performance recorded in conjunction with the mock-up is then, in turn, imported into the big data system as a new recording, to be swiftly evaluated. Instead of a closed loop approach in which the signals originally recorded are only played back one to one, the proposed architecture now also enables a substratum generated case-specifically to be consulted.23 Consequently, the number of test cases that are suitable for a simulation rises dramatically. So, thanks to modern signal data processing and digital twins, simulations and virtual reality are getting ever closer to the possibilities of a real-life test.

22 https://www.docker.com/23 http://www.ni.com/de-de/innovations/white-papers/17/altran-and-ni-demonstrate-adas-hil-with-sensor-fusion.html

hil hil hil hilhil hil hil hil

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ADDED VALUE VIA SCENARIO RECOGNITION, CREATION AND SIMULATION

Simulation

Digital twin

Test vehicle

ECU

ECU

Playback

RecordData Center

WHO IS LEADING THE RACE?

The scramble towards autonomous driving, and the continuous development of innovative vehicle functions and mobility services, is in full swing. The specialist areas connected with vehicle development have long been working with the latest agile software development and DevOps methods. Vehicle electronics, big data and smart software have become the focus for digital success in the automotive sector, i.e. DevOps IT models (design, prototype, test and deploy) will be the DNA for car builders, and the limiting factor of time has become a crucial success criterion.

New methods such as T-Systems’ big data signal processing are driving development process-es powerfully forward. In the measurement data management area, it is helping make data available 40 times more quickly, while the data is being compressed to up to 10 per cent of its original volume – loss-free. This saves costs on the big data platform. The available data provide insights into the quality of the tested vehicle functions, and they can also be used in other scenarios, such as in developing automatic situation recognition using machine learning algo-rithms, and in combination with digitally upgraded digital twins. Therein lies one of the greatest value contributions made by smart engineering, as test drives do not need to be repeated over an average of 150,000 kilometres per test. Rather, new findings can be achieved in the auto manufacturer's own laboratory environments which will optimise the control unit software.

These are important milestones in overcoming data processing challenges. It means that future scenarios, familiar to us from the cinema and TV, will become a reality faster. In the end, it will not only be private motorists who are affected as people will soon be picked up just-in-time by a self-driving taxi or taken on holiday in a minibus with sleeping facilities. Companies will also be able to optimise their business processes by integrating autonomous goods vehicles into their logistics processes, for example to deliver parcels, medication or spare parts.

The question remains open as to which market participants will be first to pave the way for this reality.

AUTHORS

WOLFGANG HOLZ… is a sales consultant in the Digital Solutions portfolio unit at T-Systems International GmbH. He is a sales expert in everything related to BI, big data and analytics. Tel.: +49 171 8643 587, Email: wolfgang.holz@t-systems.com

DR. CHRISTOPH G. JUNG… is a Principal Architect in the Digital Solutions portfolio unit at T-Systems International GmbH. He is the Tribe Lead for big data signal processing and works on analysing upscaled measurement data using massive parallel computing infrastructures. Tel.: +49 170 4146 717, Email: c-jung@t-systems.com

SASCHA LEIDIG… is the Chapter Lead for Portfolio & Partner Management at T-Systems focusing on Product Lifecycle Management (PLM). In this business area, he is responsible for the PLM portfolio strategy and is driving innovations such as digital twinning for engineers, systems engineering and the PLM cloud. Tel.: +49 175 2410 836, Email: sascha.leidig@t-systems.com

BASTIAN WYMAR… is the Tribe Lead for the data intelligence portfolio in the Digital Solutions unit at T-Systems International GmbH. There he is responsi-ble for shaping the portfolio, marketing measures and sales enabling. In this role, he is also advancing new offerings such as T-Systemsʼ Data Journey. Tel.: +49 171 2288 215, Email: bastian.wymar@t-systems.com

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CONTACT

Questions about organisation, press and marketing? Please send your enquiries to:

Christopher Link Marketing Campaign Manager Tel.: +49 170 5767 991 Email: christopher.link@t-systems.com

Imprint:© 2019

T-Systems Multimedia Solutions GmbH Riesaer Straße 5, 01129 Dresden, Germany

Rights reserved, including the rights to reprint extracts,

photomechanical reproduction (including microcopy) and analyse using databases or similar devices.

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