Digital Twin Development and Deployment on the Cloud: Developing Cloud-Friendly Dynamic Models Using Simulink®/SimscapeTM and Amazon AWS

584 Pages·2020·21.895 MB·English

by  Nassim Khaled, Bibin Pattel, Affan Siddiqui

Digital Twin Development
and Deployment on the
Cloud
Developing Cloud-Friendly Dynamic
(cid:1) TM
Models Using Simulink /Simscape
and Amazon AWS
Nassim Khaled
Bibin Pattel
Affan Siddiqui
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1
Added value of digital twins
and IoT
1.1 Introduction
This is a great time to be in the modeling and simulation business. Advances in elec-
tronics and microprocessors capabilities have enabled much faster simulations on
stand-alone PCs. Furthermore, cloud technologies have enabled massive amounts of
simulations to be carried out on the server. It also opened the door for new types of
revenuestreamssuchasSoftwareasaService(asopposedtothetraditionalmodel,Soft-
wareasaProduct).TheseadvancesenabledMathWorkstobeamongthefirstprivately
ownedsimulationandcomputationsoftwaretoexceed1billiondollarsrevenue.
For cloud service providers, it is even a greater time. Streaming services, data
storage,dataanalysis,socialmediaoutlets,andIoT-relatedactivitiesprovidedtremen-
dous business opportunities. Microsoft Azure, Google Cloud Platform, and Amazon
Web Services seem to be well positioned in the top three in terms of cloud revenue.
Thesethree areclosingon 80 billion dollars revenue in 2020.
Engineering companies and design firms are reluctant to run their simulations on
thecloud.Themainreasonforthisunderwhelmingadaptationisrootedtothecurric-
ulum culture of engineers. Based on our experience, vast majority of engineers still
believe that “cloud is an IT function.” Additional reasons and excuses to the lack of
adaptation by engineers are the lack of understanding the benefits of cloud servers,
amount of time it takes to set up and deploy cloud simulations, security concerns,
andspeedofdownloadofthemassivedatageneratedbythesimulationsontheservers.
Cloudsimulationsareameaslypartofcloudrevenue,andtheywillremainsofor
the near future. They are hugely underutilized. They have the ability to improve
engineering practices and designs by levering the massive cloud infrastructure and
existing communication, security protocols, file revision standards, and software as
servicemodelofmostsimulationcompanies.Cloudsimulationsalsooffertremendous
potential inthe world ofdiagnostics and prognostics.
Thebookisaimedtopromoteusageofmultiphysicssimulationmodelsrunningon
the cloud to improve diagnostics and prognostics. In this book, we redefine the term
digital twins as it pertains to the diagnostics and prognostics context. We believe
that the existence of several definitions of digital twin is slowing down the process
of its maturity. Business leaders in engineering firms need to understand “what is
it?”,“howcanitprovidevalue?”,and“howmuchitcoststodeploy(andmaintain)?”.
This will help them todecide toembrace ornot.
We outline the key elements needed to deploy digital twins. The hardware and
softwaretoolsneededaredescribedaswellastheprocessofdevelopinganddeploying
digitaltwins.Challengesandbottlenecksthatexistwillbetackled,andwewillpropose
currentandfuturework-arounds.
DigitalTwinDevelopmentandDeploymentontheCloud.https://doi.org/10.1016/B978-0-12-821631-6.00001-3
Copyright©2020ElsevierInc.Allrightsreserved.
2 DigitalTwinDevelopmentandDeploymentontheCloud
1.2 Motivation to write this book
The authors saw significant safety benefits that digital twins can provide to any
machine/product/process.Furthermore,theysawalackofpublicationsrelatedtofields
of self-diagnostics and prognostics as pertaining to digital twins. This is a field that
combines the understanding of the fundamental operation of electromechanical
systems, automation, and controls, in addition to communication and computer
science.
Itiscriticaltoprovideanintroductiontothebackgroundoftheexpertswhocontrib-
utedtothisbook.Thisallowsthereaderanopportunitytounderstandwhatforgedthe
motivationandopinionoftheauthors.Sevenexpertscollaboratedcloselytowritethis
book.Theytookanoathtodeliveranoriginalpublicationintheareaofsimulationand
cloud technologies to improve the safety of engineered products. It is a part of their
mission tobetterthe human lives around the world byspreading useful knowledge.
They have worked in the industrial, research and development, and academic
world. Industries they worked in include automotive, autonomous guidance and
control, battery systems, HVAC and refrigeration, electric grid control, and video
and image processing. All the experts are below 40years of age, but with a total of
60years combined experience.
Dr. Nassim Khaled is the author of two books in the fields of controls and
simulation [1,2] (both books are all-time best MATLAB e-books). He worked as an
engineering manager for controls in two US engineering companies: Cummins and
HillPhoenix.HeiscurrentlyworkingasanAssistantProfessorinPrinceMohammad
Bin Fahd University, KSA. Dr. Khaled has more than 30 filed patent applications
worldwide and24 publishedUS patents.
BibinPattelistheauthorofonebookinthefieldofcontrolsandsimulation.Heis
currentlyworkingasatechnicalexpertinKPITinthefieldofautomotivecontrol.Heis
anexpertinsoftwaredevelopmentfordiagnosticsandcontrol.BibinhasaMastersin
Mechanical Engineering.
Affan Siddiqui is currently working in Cummins Emissions Solutions as a Senior
Controls Engineer.Hespecializes insoftwaredevelopmentofcontrol anddiagnostic
algorithms of diesel engines and aftertreatment systems. He has a Masters in
Mechanical Engineering.
ChetanGunduraohasworkedacrossvariousindustriespertinenttoprocesscontrol
anddiscreteautomationcontrolindustries,developingsolutionsaroundsoftwareand
embeddedsystemebasedclosedloopandopenloopcontrolsolutionsoverhiscareer.
Chetan is currently working as a Technical Architect for Dover Corporation. Chetan
holds a Bachelor Engineering Degree in Electronics and Communication and a
Masters in Computerscience.
Naresh Kumar Krishnamoorthy has a Masters in Electrical Engineering. He is
currently a project lead in Dover Innovation Center in India. He is specialized in
diagnosticsand controls.
Jisha Prakash iscurrently pursuing her Masters inElectrical Engineering,and her
area of research isPower Electronics Optimal Control.
AddedvalueofdigitaltwinsandIoT 3
Stephen John Limbos is a lab technician in the College of Engineering in Prince
MohammadBinFahdUniversity.HehasaBachelorsinElectronicsandCommunica-
tionsEngineering. He has extensive experiencein software/hardware integration.
The authorshavemet andagreed thatmodel-based diagnostics thatarecarriedon
the cloud simulations are highly underutilized in theengineering world and have the
potential to bring significant safety and maintenance improvements for an array of
products.Thisbookbringsaframeworktostreamlinethedevelopmentofdigitaltwins
mainlyfor thediagnostics and prognosticsof machines.
1.3 Digital twins
Inthiswork,wedefinethedigitaltwinthatisusedforthepurposeofabnormalitydetec-
tion of a process, plant or machine. Detection of a potential abnormality that already
occurrediswidelyreferredtoasdiagnostics,whereasdetectionofpotentialfutureabnor-
malityisreferredtoasprognostic.Bothconceptsrequiresomeformofamathematical
modelthatmimicsthephysicalbehaviorofthesystem.
Companiesdefinedigitaltwinsbasedontheirbusinessmodel.Thisiscausingsome
confusion as well as limiting the benefits of digital twins. Below we mention two
examples of how digital twins are defined by businesses:
MathWorks define digital twins as “an up-to-date representation, a model, of an
actual physical asset in operation. It reflects the current asset condition and includes
relevanthistoricaldataabouttheasset.Digitaltwinscanbeusedtoevaluatethecurrent
conditionoftheasset,andmoreimportantly,predictfuturebehavior,refinethecontrol,
or optimizeoperation. [3].”
Boschdefinesdigitaltwinsas“connecteddevicesdsuchastools,cars,machines,
sensors,andotherweb-enabledthingsdinthecloudinareusableandabstractedway.
[4] ”
Inthisbook,wewilladoptthedefinitionpresentedbyMathWorks.Thedefinition
proposedbyBoschisonelevelofabstractionhigherthanMathWorks.Wearehoping
tohelpanswerquestionsthatmightberaisedbybusinessleaderssuchas“whatisit?”,
“how canit provide value?”, and“how much it costs todeploy (andmaintain)?”.
1.4 On-board and off-board diagnostics
On-boarddiagnostics(OBD)isatermmostlyusedintheautomotiveworld,despitethe
factthatitisapplicableinotherindustries.Itreferstohavingaprocessoron-boardof
the vehicle for the purpose of diagnosing vehicle malfunctions. Typically, there is a
sensor,actuator,orcomponentthatisservingafunction,andthereisavirtualmodel
thatispredictingtheexpectedoutcome.Theoutputsofthesetwoarecomparedanda
diagnostic decision is issued based on the difference. Fig. 1.1 shows the traditional
process todesignanddeploy a diagnosticin a vehicle.
4 DigitalTwinDevelopmentandDeploymentontheCloud
Figure1.1 Traditionalon-boarddiagnosticsetup.
The main challenge of vehicle diagnostics is the limited processing capability of
the processing unit on-board. There are usually hundreds of diagnostics running in
the electronic module of the car at each 100ms. This results in embedding limited
capability digital twins on-board of the vehicle. The design and structure of these
digitaltwinsarenotupdatedduringthelifecycleofthevehicleorthemachine(unless
thereisarecallfortheproduct).Also,theseon-boarddigitaltwinsdonotbenefitfrom
sensorydatathatmightnotbeavailableon-boardthevehicle(suchasaveragehumid-
ity, wind speed, or density of air in an area). Additionally, these on-board digital
twins will not have access to historical data for the duration of the vehicle due to
on-board memory constraints. Finally, these on-board models will not benefit from
thelearningsofthewholefleetbecauseitisisolatedfromtherestofthefleetdigital
twins.
We introduce the term off-board diagnostics (Off-BD) instead of OBD to refer to
theprocessofhavingthedigitaltwinsaswellasthediagnosticdecisiontakingplace
onthecloudorremotefromthevehicleormachine.ThestepstodesignanOff-BDare
highlighted in Fig. 1.2. The failure modes of the asset are highlighted. Then a block
diagram with inputs/outputs is drawn. An edge device sends the data to the cloud.
A virtual model for the asset is built and calibrated (MATLAB/Simulink tools are
usually used). A diagnostic algorithm is constructed. Such algorithm usually tracks
thedeviationofthephysicalassetfromthevirtualmodel.Thevirtualmodelrepresents
thenominalbehavioroftheasset.Anydeviationsfromthevirtualmodelaredeemedto
be failures if they exceed apredetermined threshold.
Self-diagnostics,OBD,andOff-BDareallnecessaryfunctionsforanyasset.These
diagnosticsaremeanttodetectcriticalorprimaryfailuresintheasset.Designingsuch
diagnostics to detect specific failure modes is an art that is not particularly taught in
depth in any branch of engineering. It usually combines the knowledge of physics,
observers and controllers,communication, andmicroprocessors.
Figure1.2 Developinganddeployingdigitaltwinsbasedonasetoffailuremodes.
AddedvalueofdigitaltwinsandIoT 5
Webelieveself-diagnosticsanddigitaltwinsgohandinhand.Asamatteroffact,
throughoutthisbook,itisimplicitlyassumedthatdigitaltwinsareusedalongsidewith
some form of a diagnostic. The mere display of data coming from assets remotely is
not considered to be digital twinning in this work. This is why we use the term Off-
BDtodescribetheprocessofhavingavirtualmodelonthecloudcoupledwithausage
ofsomefailurecriteriaoftheasset.Whentheoutputdataofthevirtualmodelandthe
physicaldatadivergepersomediagnosticlogic,afailureintheoperationoftheassetis
assumed. The user has tobe alerted of suchfailure.
InFig.1.3,wedemonstratehowOff-BDworksforonephysicalasset.InFig.1.4,
we demonstrate how Off-BD works for five similar assets that belong to the same
platform.
1.5 Modeling and simulation software
Modelinganoilrig,avehicle,anaircraft,oraspaceshuttleinavirtualenvironmentis
acomplexprocess.Suchsystemscontainmechanical,electrical,structural,chemical,
andelectroniccomponents.Inmostscenarios,separatemodelsarebuiltfortheentire
system.Forexample,acombustionmodelisbuilttosimulate thepoweroutput,heat
transfer,andemissions.Aseparatemodelisbuilttomodelthetransmissiondynamics.
Anothermodelisbuilttosimulatetheaerodynamicsofthevehicle.Similarly,another
modelisconstructedtosimulatetherobustnessofthecontrollogictocontroltheair-
handling subsystem of the engine. These models have different fidelities and mimic
partial behavior of the system. The execution time of these models can range from
few seconds tofew days.
Multiphysics models that represent the whole system are rare at best. Having a
common solver and step size for mechanical, electrical, structural, chemical, and
electronic processes make the model very difficult to handle and execute. Neverthe-
less, there are Multiphysics models that represent subsystems. These models are
usuallyused for designing software and hardwarecomponents of thesystem.
There are many simulation softwares for Multiphysics modeling. COMSOL [5],
SimScale [6], AnyLogic [7], MathWorks [8] and Ansys [9] are all powerful tools
thatcanbeusedtobuildmultiphysicsmodels.Despitetheapparentneedforsimulation
softwareinautomotive,aerospace,andengineeringcompanies,revenuesformodeling
softwarecompaniesaremediocreandseemdisproportionatewiththepotentialsavings
and innovation they enable. Ansys, a publicly traded company, had a revenue of 1.3
billion USD in 2018 [10]. While COMSOL, SimScale, and AnyLogic had 35, 5,
and 25 million USD in revenue [9].
Inthisbook,wefocusonMATLAB®andSimulink®sinceitisthemost-utilized
softwareforthepurposeofcontrolandalgorithmdevelopment.Industrialandresearch
both favor the software tools provided by MathWorks. LinkedIn lists technical
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