Setting up a Digital Twin for Real-Time Remote Monitoring of a

Cyber-Physical System

Adrien Vinel

, S

everine Durieux

, Laurent Pi

etrac

, Gl

enio Simi

ao Ramalho

and Nicolas Blanchard

Clermont Auvergne Universit

e, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000 Clermont-Ferrand, France

{adrien.vinel, severine.durieux, laurent.pietrac, glenio.simiao ramalho, nicolas.blanchard}@sigma-clermont.fr

Keywords:

Digital Twin, Real-Time, Remote Monitoring.

Abstract:

With the advent of Industry 4.0, Digital Twin technology has emerged as a pivotal advancement in industrial

applications. It enables the creation of a precise digital replica of physical systems. These Digital Twins can

be used throughout the entire lifecycle of the physical system, from initial design stages through to opera-

tion and disposal. They facilitate design optimization and enable simulations under realistic conditions. This

paper presents a case study centered around a UR3e robot, where a Digital Twin is developed using Emu-

late3D. Communication between its physical and digital counterparts is established. This setup thus enables

synchronized operation: when the physical robot executes a program, the Digital Twin replicates the actions

and responses, and vice versa. This represents the ﬁrst steps towards the use of Digital Twin technology for

real-time remote monitoring of the robot.

1 INTRODUCTION

The rise of Industry 4.0 has been marked by signif-

icant technological advancements, among which the

concept of Digital Twins (DT) stands out as an inno-

vative fusion of physical and digital realms. Initially

introduced by Michael Grieves in 2002, Digital Twin

technology has evolved into a fundamental tool across

various industrial sectors, including manufacturing,

aerospace, automotive, and healthcare. These virtual

models replicate not only the physical appearance but

also the dynamic behavior of their real-world counter-

parts, providing a critical interface for real-time mon-

itoring, diagnostics, and prognostics (Kritzinger et al.,

2018; Barricelli et al., 2019).

Digital Twins leverage the power of IoT sensors,

artiﬁcial intelligence, and machine learning to create

live, evolving simulations. These simulations enable

continuous monitoring and predictive maintenance,

thus enhancing operational efﬁciency and decision-

making capabilities (Negri et al., 2017; Tao et al.,

2018). For example, in the automotive and aerospace

industries, Digital Twins facilitate the simulation of

vehicle and aircraft performance under varied oper-

https://orcid.org/0000-0002-6167-1823

https://orcid.org/0000-0001-5284-2478

https://orcid.org/0000-0001-9791-5845

ational conditions. This capability signiﬁcantly re-

duces reliance on physical prototypes, thereby de-

creasing costs and enhancing the safety and reliability

of these high-stakes products (Jones et al., 2020).

In the manufacturing sector, Digital Twins prove

instrumental in optimizing production lines. They of-

fer detailed insights into process inefﬁciencies, bot-

tlenecks, and potential failures, enabling preventive

adjustments to maintain continuous and efﬁcient pro-

duction (Kamble et al., 2018; Lu et al., 2020). More-

over, Digital Twins are crucial in the energy sector,

where they are used to model and optimize the op-

erations of complex systems such as wind turbines

and power plants. Their application in this sec-

tor promotes sustainability and reduces operational

risks (Wang et al., 2017; Schleich et al., 2017).

Another signiﬁcant application of Digital Twins

is in the realm of maintenance. Traditional mainte-

nance strategies, which are often reactive or sched-

uled at predetermined intervals, can lead to unnec-

essary downtime and unexpected equipment failures.

Digital Twins facilitate a shift towards predictive

maintenance, where the condition of equipment is

continuously assessed to schedule maintenance just

before potential failures are anticipated. This proac-

tive approach not only minimizes downtime but also

extends the lifespan of equipment, ensuring that op-

erations run smoothly and costs are kept under con-

410

Vinel, A., Durieux, S., Piétrac, L., Simião Ramalho, G. and Blanchard, N.

Setting up a Digital Twin for Real-Time Remote Monitoring of a Cyber-Physical System.

DOI: 10.5220/0012995800003822

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics (ICINCO 2024) - Volume 2, pages 410-417

ISBN: 978-989-758-717-7; ISSN: 2184-2809

trol (S

oderberg et al., 2017; Schluse et al., 2018).

Furthermore, Digital Twins signiﬁcantly aid in the

customization and testing of products in a virtual en-

vironment before actual production, leading to inno-

vations in product design and functionality. They also

play a crucial role in training and simulation, where

operators are trained on virtual models, thus reduc-

ing training costs and exposure to hazardous environ-

ments (Glaessgen and Stargel, 2012).

For those interested in more in-depth examples

and broader applications of Digital Twins, compre-

hensive review articles provide extensive discussions

on the impacts and implementations of Digital Twin

technologies across various industrial sectors (Khan

and Turowski, 2016; Tao et al., 2018).

This study focuses on the use of Digital Twin for

real-time remote monitoring of robots. To achieve

this, real-time communication between the physical

and digital twin must be established. In addition, the

twins should be able to replicate in real-time each

other’s behavior. Moreover, the architecture should

allow an operator to remotely take control of the phys-

ical twin via its digital twin. This paper presents

the ﬁrst steps towards this goal. For this purpose,

it presents a practical application involving a UR3e

robot and the Emulate3D software. First, the global

architecture of the Digital Twin will be described, fol-

lowed by a detailed discussion of a practical test case.

This work aims to demonstrate the capabilities of Dig-

ital Twins in enhancing real-time system monitoring

and operational tuning for improved responsiveness

and adaptability.

2 GLOBAL ARCHITECTURE

The implementation of the Digital Twin technology

in this study focuses on a compact robotic system

equipped with a grip and a camera. In addition, a con-

veyor adjacent to the robot transports objects within

its reach. This section will describe the architecture

of both the Physical Twin and the Digital Twin.

The UR3e robot (Universal Robot, 2024), a small

industrial robot arm cited frequently in academic and

industrial applications, is known for its versatility and

ease of use. It comes with a teach pendant that al-

lows and simpliﬁes the programming and control of

the robot, making it suitable for a wide range of tasks

from assembly to welding. Its affordability, com-

pact size, and adaptability make it an ideal candidate

for experimental and educational projects in academic

settings (Wolniakowski et al., 2021; Mustaﬁn et al.,

2023; Abbyasov et al., 2024).

A review conducted in (Konstantinov et al., 2023)

evaluated various Digital Twin softwares according to

the Industrial Internet Consortium’s criteria (Yi et al.,

2015). In this study, Emulate3D and Visual Compo-

nents emerged as leaders due to their advanced capa-

bilities and compliance with numerous criteria. In this

work, the software used is Emulate3D. To the authors’

knowledge, this software has been scarcely used to

develop Digital Twins and it has mostly been used for

simulation purposes. For example, McGinnis and his

co-authors (McGinnis et al., 2021) have used the soft-

ware to produce a physic-based model of a robotic

consolidation cell. This allowed the authors to de-

velop realistic cycle time distributions. In addition,

in their work Zhao and his co-authors (Zhao et al.,

2022) used Emulate3D to simulate and optimize the

design of a large scale mobile phone assembly pro-

duction line. For this work, Emulate3D was princi-

pally used as a simulation tool. It allowed retriev-

ing various parameters, thus enabling the computation

of deﬁned KPIs and identifying potential bottlenecks.

Nevertheless, to our best knowledge, no Digital Twin

with real-time communication has been implemented.

2.1 Physical Twin (PT)

The physical setup revolves around a UR3e robot

equipped with a suction gripper from Schmalz Vac-

uum and a high-deﬁnition camera from SICK (see

Table 1 for more details). The system’s versatility is

enhanced by a programmable interface provided on

a tablet that accompanies the robot, allowing control

and programming capabilities. Furthermore, the as-

sembly includes a conveyor system managed via a

Human-Machine Interface (HMI), complemented by

an optical sensor that detects the objects on the con-

veyor.

This setup is controlled through a Programmable

Logic Controller (PLC) from Schneider, which en-

sures communication and operational synchroniza-

tion among the various components. The integration

of these components is facilitated by an IO-LINK sys-

tem that guarantees robust data transmission and sys-

tem reliability.

The communication dynamics within this system

are depicted in Figure 1, illustrating how the various

elements interact within the network:

1. The camera and suction gripper are controlled by

the control interface of the robot. Using this in-

terface it is possible to activate or deactivate the

gripper, as well as to detect loads using the cam-

era (this detection is programmed via SOPASair

but it falls out of the scope of this work).

2. The UR3e robot communicates essential opera-

tional data such as joints’ angle, tool position, suc-

Setting up a Digital Twin for Real-Time Remote Monitoring of a Cyber-Physical System

411

tion gripper status to the PLC. In addition, some

channels are open so that it can receive data from

the PLC.

3. Data from the optical sensor about the status of

the conveyor (blocked or cleared) is sent directly

to the PLC, enabling real-time adjustments.

4. The conveyor sends its status as well as its speed

to the PLC, and the PLC can be programmed to

change it.

5. The HMI plays a crucial role in visualizing the

status of the optical sensor and allows operators

to adjust the conveyor settings directly.

Figure 2 captures the experimental setup in a real-

world scenario, visually representing the integration

and placement of different components within the

system.

Table 1: Hardware setup.

Component Supplier Model

Robot Universal UR3e

Robots

Camera SICK InspectorP62x

Gripper Schmalz Vacuum ECBPMi

Conveyor Norelem 95300

Optical sensor IFM O8H216

PLC Schneider M251MESE

IO-Link IO-Link AL1342

2.2 Digital Twin (DT)

In this project, the Digital Twin is built using the Em-

ulate3D software, a powerful tool that allows for the

recreation of the Physical Twin using detailed CAD

models. Emulate3D’s advanced physical engine facil-

itates the precise mimicking of the physical behaviors

observed in real-world settings, thus providing an au-

thentic and dynamic simulation environment (see Ta-

ble 2).

In this study, the CAD models from the different

parts of interest (namely the UR3e robot, the suction

gripper and the conveyor) were taken from their re-

spective suppliers. The CAD model for the support

plate was designed manually. Moreover, to reproduce

the behavior of the physical optical sensor, a sensor

element from the software has been added its local-

ization.

During the assembly of these components within

Emulate3D, attention to detail was paid to ensure that

all parts were positioned in the same conﬁguration as

their physical counterparts (see Figs. 2 and 3). Using

the software’s tools, the kinematic joints were repro-

duced, thus recreating the dynamic interactions be-

tween various parts of the assembly. Special attention

Table 2: Software used.

Software

Automation software Machine Expert 2.0

Camera software SOPASair

Digital Twin software Emulate3D 2022

was paid to input accurate mechanical properties into

the software, such as the joints’ angle limits, speed

limits, and acceleration limits, to ensure the Digital

Twin’s performance closely matches that of the Phys-

ical Twin.

Emulate3D’s functionality extends beyond mere

simulation; it is equipped to facilitate robust com-

munication with Programmable Logic Controllers

(PLCs) using protocols such as Modbus. This is crit-

ical for real-time data exchange between the Digital

Twin and its physical counterpart, enabling synchro-

nized operations and adjustments. Moreover, by es-

tablishing a secure network connection, the system

enhances the feasibility of remote interactions be-

tween the two twins. Thus maximizing the utility of

the Digital Twin for remote monitoring and control

applications.

3 TEST CASE STUDY

The advent of Digital Twin technology leads to new

opportunities for enhancing remote monitoring and

maintenance processes within industrial settings. The

capability of Digital Twins to accurately replicate and

simulate physical systems presents an innovative ap-

proach to handling incidents on production lines.

Traditionally, when an incident occurs, it necessi-

tates the stoppage of production activities and wait-

ing for an on-site intervention. This approach often

leads to signiﬁcant downtime and loss of productiv-

ity. However, with the integration of Digital Twin

technology, these challenges can be addressed more

efﬁciently. Indeed, a Digital Twin allows for the de-

tailed replay and analysis of the events that led to the

incident. This not only aids in swiftly identifying the

causes of issues on the production line but also helps

the planning of necessary repairs and interventions

without the immediate need for on-site presence.

Furthermore, using the detailed data from the ac-

tual on-site conditions, engineers can use the Digi-

tal Twin to perform simulations to ﬁnd new, optimal

operating parameters. This process ensures that once

the production line is ready to restart, it operates un-

der the best possible conditions, minimizing the like-

lihood of recurring issues.

Moreover, in scenarios where adjustments to the

production processes are required, Digital Twin tech-

ICINCO 2024 - 21st International Conference on Informatics in Control, Automation and Robotics

412

Figure 1: Schematic of the different communications for the Physical Twin. The dashed lines represent communications via

the IO-Link. The blue squares depict the elements that constitute the Physical Twin.

Figure 2: Experimental setup of the robot in the real world.

Figure 3: Comprehensive digital setup of the robot in Emulate3D, illustrating the detailed conﬁguration and component

integration.

Setting up a Digital Twin for Real-Time Remote Monitoring of a Cyber-Physical System

413

nology enables the remote reprogramming of the pro-

duction line. This functionality is particularly crucial

as it allows for the continuation of operations, even

if at a reduced capacity, thus signiﬁcantly mitigating

the impact of downtime and minimizing operational

losses.

The test case study presented here is built upon

the physical setup described earlier. It involves a sce-

nario where objects, transported via a conveyor, are

identiﬁed by the camera system and then handled by a

robot, which sorts them into their designated contain-

ers. This practical application is designed to demon-

strate the initial steps towards implementing real-time

monitoring and adaptive control using a Digital Twin.

The approach to achieving effective real-time

monitoring with a Digital Twin can be decomposed

into two main stages:

1. Synchronization of the Digital Twin with the

Physical Twin. This stage involves setting up the

Digital Twin to accurately follow and replicate

the real-time actions and statuses of the Physical

Twin. This synchronization is crucial for ensuring

that the Digital Twin provides a faithful and up-to-

date reﬂection of the physical setup, allowing for

immediate and accurate response strategies.

2. Enabling the Physical Twin to follow adjustments

made in the Digital Twin. Once the initial syn-

chronization is achieved, the next step involves

adapting the system so that changes or optimiza-

tions suggested by simulations in the Digital Twin

can be applied back to the Physical Twin. This

bi-directional communication ensures that any en-

hancements or modiﬁcations obtained from the

Digital Twin’s simulations are effectively imple-

mented in the physical setup, enhancing opera-

tional efﬁciency and adaptability.

3.1 DT Following PT

Achieving real-time remote monitoring involves en-

suring that the Digital Twin can accurately replicate

the actions and status of the Physical Twin. This sec-

tion details the initial phase of this process.

The ﬁrst step in this synchronization process in-

volves programming the Programmable Logic Con-

troller (PLC) to halt the conveyor system when the

optical sensor detects an object. The behavior of the

robot is then programmed using its control interface

and the associated programming module. The pro-

grammed behavior sequence is as follows:

• Initialization: The robot is set to a default posi-

tion, ready to start the operation cycle.

• Object Detection: If the conveyor stops, the

robot uses its camera to detect the object on the

conveyor.

• Interaction: After detecting the object, the robot

moves to the object’s location, activates the grip-

per to secure the object, and then moves it to its

designated target location.

• Release and Reset: After placing the object, the

gripper is deactivated, and the robot returns to its

initial position.

• Idle State: If the conveyor does not stop, the

robot remains in a waiting state, ready to react to

the next trigger.

Emulate3D, the software used for the Digital

Twin, supports robust communication with PLCs.

This feature allows for the transfer of operational data

from the Physical Twin to the Digital Twin in real-

time. In what follows, the communications needed

for the DT to mimic in real-time the PT are described:

• Robot Position: The robot position in the Digital

Twin can be obtained by using the joints’ angles.

They are retrieved by the software using the Mod-

Bus protocol. To ensure good agreements, the an-

gles are rounded in milli-radians.

• Conveyor Status: The software retrieves the con-

veyor status and speed using the same protocol.

The status is a simple boolean, and the speed is

retrieved in rounded mm/s.

• Gripper Status: The software also retrieves the

gripper’s status. The status is a simple boolean.

• Sensor Status and Object Creation: Similarly

to the other part’s status, the sensor’s status is a

boolean retrieved by the software. In addition,

as soon as the gripper is activated, an object is

created on the conveyor and transferred instanta-

neously below the robot’s tool.

All the data are refreshed by the software every micro-

second. This setup thus ensures that the virtual twin

mirrors the physical setup accurately.

3.2 PT Following DT

Achieving real-time remote monitoring also requires

the Physical Twin to accurately replicate the move-

ments dictated by its Digital Twin. This synchro-

nization ensures that improvements or modiﬁcations

tested virtually can be applied effectively in the phys-

ical environment. This stage is decomposed in several

steps.

ICINCO 2024 - 21st International Conference on Informatics in Control, Automation and Robotics

414

Step 1: Replicating Realistic Behavior in the Digi-

tal Twin. The initial step involves programming the

Digital Twin to mimic real-world physics accurately.

In this simulation, objects are generated within the

boundaries of the conveyor, appearing at random po-

sitions and intervals of time (e.g. every 7 seconds ±

1 second). When an object is detected by the opti-

cal sensor, the conveyor halts, prompting the robot

to approach and interact with the object. The robot

then activates its gripper, secures the object, and relo-

cates it to a speciﬁed slot, mirroring the tasks it would

perform in the physical world. In addition, to ensure

the same starting conditions, the digital robot is pro-

grammed to return to the same default position that

its physical counterpart would assume at the start of

operations.

Step 2: Transmitting Operational Data to the

Physical Twin. The second step focuses on the

transmission of operational data from the Digital

Twin to the Physical Twin via the PLC. This process

is crucial for the physical robot to emulate the actions

performed by the Digital Twin. Unlike the previous

stage, the joints’ angles cannot be used in this step as

they cannot be modiﬁed using the PLC. However, it

is possible to solve this issue by using the fact that

both twins are programmed to go to the same default

position at the beginning of their starts. Indeed, by

communicating the tool’s displacement vector (with

respect to its default position) it is possible for the

Physical Twin to move the tool the same way as its

digital counterpart (Fig. 4). As the reference conven-

tions differ between Emulate3D and the interface con-

trol of the robot, attention has been paid to ensure that

the vector communicated is consistent with the con-

ventions used by the UR3e robot. Nevertheless, due

to the fact that the robot has been assembled in the

same conﬁguration as in the physical world and that a

vector is communicated and not the absolute position,

no calibration is needed. Indeed, the transformations

from one convention to another can be found heuris-

tically. Moreover, the conveyor’s state as well as the

gripper’s state are communicated to the robot.

Step 3: Implementing Adjustments in Real-Time.

Finally, the physical system is programmed to ad-

just its operations based on the input from the Digital

Twin. This involves updating the robot’s position and

the state of the gripper in real-time as per the simula-

tions run on the Digital Twin.

3.3 Towards Real-Time Monitoring

The developments presented so far have established a

system where one twin can mirror the actions of the

other, controlled by a boolean set at the initialization

of the program. This setup is a preliminary stage to-

wards achieving a real-time monitoring system.

The next step is the implementation of a dynamic

boolean control for real-time monitoring. The cur-

rent system’s dependence on a static boolean value,

checked only during the program launch, limits the

ﬂexibility needed for real-time responsiveness. To

overcome this limitation, a few changes need to be

implemented on the programs architectures to allow

the dynamic checking and updating of the boolean.

This change will allow changes in the Digital Twin or

operator inputs to instantaneously inﬂuence the Phys-

ical Twin’s actions.

A few more changes need to be implemented to

reach effective real-time monitoring system. Indeed,

at this stage, when the PT is following the DT, the ob-

ject’s position is the one in the virtual realm. Thus, the

codes need to be changed slightly so that even when

the PT is following the DT the sensor’s status is trig-

gered by real events, the object’s position is from the

physical realm, and the assigned slot of the object too.

This would open the door to real-time operational

control by remote operators. In practical terms, this

means that an operator could, from a remote loca-

tion, directly interact with the Digital Twin’s interface

to initiate changes, which would be immediately re-

ﬂected in the Physical Twin. For example, if an op-

erational inefﬁciency or a potential fault is detected,

the operator can adjust relevant parameters such as the

angles’ limits directly in the Digital Twin. This would

allow the Physical Twin to still function by imitating

its digital counterpart, thus respecting the limitations

imposed. In addition this prevent any need for the re-

programming of the robot.

This real-time monitoring and control framework

not only allows for immediate operational adjust-

ments but also paves the way for more proactive main-

tenance strategies. With operators able to monitor and

modify the system remotely, potential issues can be

addressed before they escalate into critical failures,

thus reducing the need for extensive on-site interven-

tions. This proactive approach not only conserves re-

sources but also extends the lifespan and efﬁciency of

the equipment.

Setting up a Digital Twin for Real-Time Remote Monitoring of a Cyber-Physical System

415

Figure 4: Schematic of the displacement vector illustrating the data transformation and synchronization process between

the Digital and Physical Twins. This schematic highlights how displacement vectors are used to ensure that movements are

replicated accurately in the physical setup, aligning with the simulations performed in the Digital Twin environment.

4 CONCLUSIONS AND FUTURE

WORKS

In this work, the ﬁrst steps toward real-time remote

monitoring of a cyber-physical system are presented.

The test case considered is revolving around a UR3e

robot which moves objects that are transported to it,

and places them in their assigned place. The follow-

ing results were obtained:

• A Digital Twin of the physical system was built

using the software Emulate3D. This virtual model

has proven to be an effective replica, accurately

mirroring the physical behaviors and operations

of the physical robot.

• Robust communication between the Physical

Twin and Digital Twin was successfully achieved,

enabling data exchange and synchronization be-

tween the two twins.

• Depending on the operator’s choice, either the

Digital Twin imitates the movement and behavior

of its physical counterpart, or vice versa.

To enable real-time monitoring, the next step is

to modify the different programs so that the operator

is able — whenever it is needed — to change which

twin is following the other on the ﬂy. These modiﬁca-

tions should be straightforwards considering the ease

of use of the robot and Digital Twin software. Once

these modiﬁcations implemented, it will be possible,

for an operator to remotely take control of the Physi-

cal Twin. And then perform the modiﬁcations needed

on the Digital Twin due, for example, to an incident

on the production line. These modiﬁcations can then

be applied directly by the robot, thus allowing the pro-

duction line to still run rather than waiting for the on-

site intervention.

The developments discussed in this paper open the

way for broader implementation and deeper integra-

tion of Digital Twin technology in industrial settings,

promising signiﬁcant improvements in system moni-

toring, maintenance, and management.

ACKNOWLEDGMENTS

The authors acknowledge the support from the ”In-

dustrie responsable” project ﬁnanced by Institut

CARNOT ”M.I.N.E.S.”.

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