=Paper=
{{Paper
|id=Vol-2739/paper_8
|storemode=property
|title=REPLICA: A Solution for Next Generation IoT and Digital Twin Based Fault Diagnosis
and Predictive Maintenance
|pdfUrl=https://ceur-ws.org/Vol-2739/paper_8.pdf
|volume=Vol-2739
|authors=Rosaria Rossini,Davide Conzon,Gianluca Prato,Claudio Pastrone,João Pedro Correia dos Reis,Gil Gonçalves
|dblpUrl=https://dblp.org/rec/conf/sam-iot/RossiniCPPRG20
}}
==REPLICA: A Solution for Next Generation IoT and Digital Twin Based Fault Diagnosis
and Predictive Maintenance==
https://ceur-ws.org/Vol-2739/paper_8.pdf
REPLICA: A Solution for Next Generation IoT and
Digital Twin Based Fault Diagnosis and Predictive
Maintenance
Rosaria Rossini, Davide Conzon, Gianluca Prato, João Reis, Gil Gonçalves
Claudio Pastrone SYSTEC, Research Center for Systems and Technologies
IoT and Pervasive Technology Area Faculty of Engineering, University of Porto
LINKS Foundation Porto, Portugal
Turin, Italy {jpcreis , gil}@fe.up.pt
{name.surname}@linksfoundation.com
Abstract—Nowadays competitiveness goes through several as- monitoring a replica instead of the real object, a perfect virtual
pects: digitalization, productivity and environmental impact. replica that interacts with both humans and machines: a Digital
Technology is advancing fast and helping industries to obtain Twin. The concept of “twin” is originally derived from Na-
more and more detailed data about their processes and equip-
ment. In fact, the possibility to monitor and control each part tional Aeronautics and Space Administration (NASA)’s Apollo
of the process is a strong base on which a more intelligent Project when the aircraft’s twin body was a real physical
and focused control can be built. Technology advance brings system [1]. Twin models help astronauts and staffs make
innovation and the possibility to manage the production in decisions under emergency situations. Digital twin integrates
terms of ”near future” through AI prediction and decision- the life cycle of a machine [2], and achieves a closed loop
making support. Forecasting demands and planning production,
optimizing process by reducing costs and improving efficiency and optimisation of the machine design, production, operation,
without corrupting the quality of the product is a big challenge and maintenance, etc. In Magargle et al. [3], a multi-physical
at the plant level. In this paper, a flexible, scalable architecture twin model is built to monitor the status of the brake system
for intelligent digital twin realization called REPLICA has been through multiple angles. NASA hopes to realise the health
proposed to cope with such problem and help industries to management and residual life prediction of the aircraft by
advance and discover possible optimizations. This architecture
sits on top of two European projects, namely CPSwarm and building a multi-physical, multi-scale Digital Twin model [4],
RECLAIM, where their contribution focus on distributed sim- furthermore serveral roles are envisioned for Digital Twin in
ulation and optimization, and Adaptive Sensorial Networks, the industry 4.0 scenario [5].
correspondingly. As a validation process, a hypothetical use case The present paper focuses on the proposition of an intel-
is presented, detailing the key differentiating points and benefits ligent digital twin architecture called REclaim oPtimization
of the proposed architecture.
Index Terms—IoT, Digital Twin, AI, Fault Diagnosis, Predictive
and simuLatIon Cooperation in digitAl twin (REPLICA), that
Maintenance focuses on two important aspects: 1) plug’n’play of models
on demand and 2) Workflow design to orchestrate the models
I. I NTRODUCTION used in the Digital Twin itself. Aspect (1) aims to ease
the integration and removal of models into digital twins,
In the era of Industrial Internet of Things (IIoT) and Industry whenever a new version of the software is available or it
4.0, complex electromechanical systems can be equipped with performs any necessary correction in the used models. The
a variety of sensors providing new opportunities for the goal of the latter aspect is dedicated to the creation of a
development of Health Monitoring and Management Systems. pipeline that can manage the flow of data among all the
These new opportunities target an optimum exploitation of available models. These models might be from pure data
available information in order to maximize the performance processing and decision making, from sensor and actuator
of the machinery and optimize the process. Focusing on the integration, to third party synchronization with information
increase of production reliability and safety, as well as on the systems such as Manufacturing Execution Systems (MES)
reduction of costs, there is an ever increasing industrial need or Enterprise Resource Planning (ERP). Most digital twins,
not only for accurate and on time online diagnostics, but also and in particular intelligent digital twin architectures focus
for a robust and early estimation of the Remaining Useful Life on providing the best set of models that one should have
(RUL) of the defected components, within a high confidence to accomplish, e.g. predictive maintenance, from the type of
interval, independent of the operating conditions. simulation required to machine learning models that should be
For this reason, and many others, a new concept of ’inter- refined based on newly acquired data. Furthermore, REPLICA
action’ with the process arose; a concept of controlling and can allow a flexible and distributed deployment in such a
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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way that both completely cloud or mixed edge/cloud solutions In [11], [12] and [13], the authors show how it is possible
deployment are accepted, with respect to the need of the to build a DT of machines and systems of systems to allow
specific application. autonomous smart manufacturing, but these works, while
However, these architectures are normally rigid and do not interesting, are not specifically presenting a solution for fault
support changing software models or even easily set up the diagnosis and predictive maintenance.
orchestration of those resources. Fixed data flows are generally The authors of [14] introduce a solution for predictive main-
hardcoded, meaning that if an implementation needs to be tenance of computer numerical controlled machines, based
modified, changes in code are required. This is often not on DT. They demonstrate how the exploitation of a DT for
recommended since these changes might negatively influence predictive maintenance can provide better results compared to
the stability of the system. To this intent, an architecture that more traditional approaches. Even if this work provides a good
addresses these challenges is presented. example of application of DT for predictive maintenance, it
The paper is organized as follows: in Section II, the authors doesn’t aim to present a solution that can be leveraged in other
introduce a literature review about predictive maintenance and scenarios.
fault diagnosis based on digital twin. Complementary, Section In this paper, the authors intend to propose a novel ar-
III presents the core technologies of the solution presented chitecture that supports several features missing in the other
in this paper. Section IV describes the architecture of the solutions presented above. Specifically, the proposed solution
solution proposed and Section V presents a first prototype is not based on a set of fixed components but it can integrate
implementation followed by Section VI in which a possible heterogeneous modules, in terms of Internet of Things (IoT)
application is described. Finally, Section VII concludes the sensors, AI algorithms and simulation tools, easing its cus-
paper by summarizing and discussing the work. tomization in different use-cases. Furthermore, thanks to the
flexibility guaranteed by the distributed nature of the system,
II. L ITERATURE R EVIEW the setting-up of the platform can easily be adapted to the each
With the rapid advancement of Cyber-Physical Production specific industrial infrastructure, selecting the most suitable
Systems, Artificial Intelligence (AI) and IIoT, Digital Twin mix of edge/cloud deployed components.
(DT) has gained increasing attention due to its capability to Moreover, the proposed architecture can support the creation
adapt and replicate the industry processes. Accordingly to at runtime of workflows both among the AI modules as well
these changes, many different DT architectures have been pro- as between the IoT sensors and the models. This drastically
posed to realize several use cases in an intelligent and complex reduces the time needed to run and collect results from the
production system. Industrial AI [6] brings to the processes AI algorithms. In these terms, a possible process optimization
self-aware, self-adapt, and self-configure functionalities and can be quickly evaluated and eventually discarded if not
facilitates the integration of the DT. appropriate. Finally, all the entities (sensors, AI modules and
In [7], the authors propose to insert an intelligent DT in simulators) can be substituted following a plug&play approach
the Cyber Layer architecture. The concept has been partially that ease the adaption of the system to the changes in the
realized with two industrial use cases, namely a modular pro- physical world.
duction system as well as a metal forming industrial process
III. C ORE TECHNOLOGIES
to show its potential and gains over the challenges in Cyber-
Physical System (CPS), i.e., synchronization throughout the A. RECLAIM platform
lifecycle of a cyber-physical production system; development Following the industry 4.0 paradigm, the business models
of the DT, which can contain different models; the interac- of manufacturing companies need to be transformed, resetting
tion between DT, both for the purpose of co-simulation and their strategies to improve productivity and quality. The current
operation data exchange; and the active data acquisition. maintenance strategies often require the user to manually
In [8], the authors present a methodology for enabling analyse data collected to extract useful information from them
DT using advanced physics-based modelling in predictive and, furthermore, periodic human inspection is required to
maintenance. This methodology for advanced physics-based assess the real condition of the assets monitored.
modeling aims to enable the DT concept in predictive main- Currently, the lack of continuous operation and health status
tenance application and consists of two main points: digital monitoring tools and predictive maintenance solutions lead
model creation and DT enabling. Then, the user is able to to unpredictable situations in industry like sudden machine
define, create and utilize the digital model of a resource, as operation failures. In this case, the current common procedure
well as its DT. The integration of DT and deep learning in CPS is to ask the intervention of technicians, which then try to
environment has been also proposed in [9] for the development repair and solve the problem. This causes several problems:
and realization of smart manufacturing. it is time-consuming; it leads to production delays since the
In [10], the authors present solutions for fault diagnosis machine is stopped until it is not repaired; it doesn’t support
based on DT. The paper includes an experiment and interesting resources distribution. The industry 4.0 paradigm goes in the
results obtained with the software proposed. Compared to direction to address such problems through different actions: 1)
the solution presented in this paper, this work has a limited re-manufacturing systems for material and resource efficiency,
flexibility since it is only suitable for fault diagnosis. 2) increased flexibility in changing machine operation purpose,
56
3) application of big data analytics techniques, and 4) pre- number of CPSs used, leveraging evolutionary design method-
dictive analytics and model-based forecasts and optimization ologies. In the latter case, candidate parameter sets are ranked
procedures, based on completely data-driven processes. based on a fitness score computed after the controller was
These four suggestions have been the funding principles executed with those parameters in a predefined environment.
of the RE-manufaCturing and Refurbishment LArge Industrial Successful parameter sets are then adapted to produce a new
equipMent (RECLAIM) concept definition. The main objec- generation of candidates to be tested. This is a high time- and
tive of the project is to increase productivity, extending the resource-consuming process, which requires a high number of
lifetime of the machines and reducing the time and cost of simulation runs. To address this, the CPSwarm solution allows
machinery refurbishment and/or re-manufacturing. This objec- to parallelize the execution of these simulations, reducing the
tive will be achieved designing and developing a set of tools times required to complete an optimization. For this objective,
supporting several activities: from the monitor of machines’ the Simulation and Optimization Environment has a network-
health status, to the implementation of adequate recovery based architecture, allowing to parallely use a set of STs
strategy (e.g., refurbishment, re-manufacturing, upgrade, main- distributed on different machines [16]. This architecture has
tenance, repair, recycle, etc.). To achieve this, the RECLAIM been implemented leveraging the eXtensible Messaging and
outcomes will include two main components: an Adaptive Presence Protocol (XMPP) protocol, already tested executing
Sensorial Network used to collect data and a Decision Support multiple simulations on Robot Operating System (ROS)-based
Framework (DSF) for optimization based on different criteria. STs, i.e., Stage, Gazebo and Virtual Robot Experimentation
Specifically one of the technologies supporting the DSF is the Platform (V-REP). In the last release of the software (available
proposed REPLICA where simulation and optimization is used as open-source on github1 ), a set of technologies have been
for fault diagnosis. The Adaptive Sensorial Network is one of integrated to improve its scalability and easy-to-use, i.e.,
the key elements to be used in the proposed architecture and docker and Kubernetes. Such final release has been tested,
is seen as an entry point for the essential data to be used. showing that it is able to scale till 128 SMs and that the time
required to complete one optimization is inversely proportional
B. CPSwarm Simulation and Optimization Environment to the number of STs used. Finally, a proof of concept has
demonstrated the ability to deploy the controller with the
As indicated in [15], the CPSwarm Workbench - the set optimized parameters onto CPSs.
of tools released by the project for the development of CPS The concept of distributed simulation and optimization
swarms applications - includes also a Simulation and Opti- is brought to the proposed REPLICA architecture by the
mization Environment, used to evaluate the performance of CPSwarm results and the whole orchestration process and
a swarm solution. Such solution is composed mainly by: the main building blocks are inspired by this project.
Simulation and Optimization Orchestrator (SOO), which over-
sees the simulation and optimization tasks; a set of Simulation IV. A RCHITECTURE
Managers (SMs), which provide common Application Pro-
gramming Interface (API) to control heterogeneous Simulation This section introduces the REclaim oPtimization and sim-
Tools (STs); and an Optimization Tool (OT) used to perform uLatIon Cooperation in digitAl twin (REPLICA) architecture
the optimization processes. The network-based architecture is that has been designed to provide an infrastructure and be
depicted in Fig. 1. used for Digital Twin-based fault diagnostics and predictive
maintenance solutions, which can be easily deployed and
customize in different Industrial IoT environments.
REPLICA is composed by several modules (shown in
Figure 2), mainly subdivided in two blocks: Backend and
Frontend. The first one contains three main components:
Artificial Intelligence (AI) Environment that hosts the AI
modules, Digital Twin Orchestrator (DTO) that is used to
orchestrate the operations done by the REPLICA and the Sim-
ulation Environment that is a distributed environment includ-
ing several heterogeneous simulators deployed into different
machines. The latter one instead contains two applications:
one devoted to show the results obtained and another one for
Fig. 1. Network-based Architecture from CPSwarm for Distributed Optimiza- the configuration of the component. These modules will be
tion and Simulation. described in the remainder of this section.
As explained in Section I, the DT concept concerns the
Such environment is useful both to simulate the behaviour
integration of three main components: the data collected by
of a designed swarm solution in a ST, leveraging the ST’s
IoT sensors; the realistic models of the real devices and the
Graphical User Interface (GUI) to evaluate its behaviour; and,
on the other side, to optimize the controller parameters of 1 https://github.com/cpswarm/SimulationOrchestrator/wiki/Simulation-and-
algorithm/module, and possible aspects of the problem, i.e., the Optimization-Environment
57
simulators data produced by the AI algorithms (for example to
simulate failures); 4) receive from the simulators the produced
results. Finally, the Simulation Environment supports for each
integrated simulator one advanced Simulator GUI that allows
to monitor the simulated device. This GUI is the one integrated
in the simulator, which provides a graphical representation
(also 3D) of the simulated device.
Finally, in REPLICA the AI algorithms are hosted and
executed in the AI Environment. This environment allows to
host and run heterogeneous algorithms for fault diagnosis and
predictive maintenance. Besides the algorithms, the environ-
ment also hosts a module called AI engine. This module has
the objective to orchestrate the algorithms creating the needed
workflows among them. Furthermore, the AI Engine uses the
API provided by the DTO to interconnect the AI modules with
the machine models and the data coming from the shop-floor.
The architecture is completed by the two interfaces in the
Frontend: the OutputMonitor GUI is used to monitor in real
time the results produced by the running solutions in a user
friendly interface. Instead, the Configuration GUI is leveraged
by the users of the system to configure the AI engine for the
needed tasks.
The proposed architecture aims to provide the following key
Fig. 2. REPLICA Architecture. features: 1) Allowing the integration of heterogeneous compo-
nents in terms of sensors data collected from the field, AI algo-
rithms and simulators running accurate machine models in the
synchronization with those using the data collected; and a set shop-floor; 2) Supporting the creation at runtime of workflows
of AI modules connected to these models. REPLICA fully not only among both AI modules and the IoT sensors, but also
supports this concept, providing the infrastructure to integrate among themselves; 3) Supporting the plug&play at runtime
these technologies. of the IoT sensors, the AI modules and machine models,
In REPLICA, the Digital Twin Orchestrator (DTO) is the without the need to restart the system; 4) Easing the adaptation
module in charge to manage all the IoT data flow coming from of the digital twin to the changes in the physical world; 5)
the field: machinery data, historical data and other data from Enabling a flexible and distributed deployment: supporting
legacy systems already present in the shop-floor. As described both completely cloud or mixed edge/cloud solutions, based
in Section III, these data are flowing through a component on the need of the specific application.
of the RECLAIM platform, the Adaptive Sensorial Network. A first partial implementation of the proposed architecture
Furthermore, the DTO is in charge to create the correct flow and a set of possible future works for the part not yet
among the AI modules running in the AI environment and implemented is presented in the next section.
the machine models running in the Simulation Environment.
Finally, the DTO oversees the storing and organization of V. P RELIMINARY IMPLEMENTATION AND FUTURE
the processed and simulated data, which are saved in a local PROSPECTS
database.
In REPLICA every machinery of interest has a corre- This section presents the first prototype of the proposed
sponding realistic model running in one of the simulators architecture. The solution is a combination of newly developed
integrated in the Simulation Environment. Specifically, the components and the evolved version of components already
Simulation Environment is a distributed environment based on developed in previous European Union (EU) projects. For
the one presented in Section III-B. Similar to what has been the new components, this section will introduce only some
presented for the original solution about swarm intelligence, possible technologies that the authors are evaluating and
the environment supports a set of heterogeneous simulators testing so far to leverage and implement the architecture, while
distributed in different machines. Each of these simulators for the existing components a more concrete implementation is
is wrapped by a Simulation Manager, and the role of this presented. More specifically, the software already implemented
component is to abstract the functionalities provided by the is one algorithm for predictive maintenance, one for fault
simulators using the standard API exported by the DTO. In diagnosis and a distributed simulation environment already
this way, the DTO can: 1) control the simulators to run the developed in CPSwarm, while the components yet to be
required simulations; 2) inject the data needed to keep the implemented are the AI environment, the AI engine, the DTO,
models synchronized with the real machines; 3) inject in the the Configuration GUI and the OutputMonitor GUI.
58
The authors have chosen to base the AI environment on a case, the deployment of the containers, the interconnection of
docker container based solution. Each predictive maintenance the components and the workflow will be handled by tools
and fault diagnosis algorithm will be wrapped in one container. included in the framework. At the moment of writing, the
In this way the AI environment will support the integration of final solution to be used is still under evaluation and the
AI modules based on different technologies. authors are investigating if Acumos AI satisfies all the needed
Two examples of solutions currently supported in the AI requirements of the AI environment, particularly focusing on
environment are the fault diagnosis and predictive maintenance the possibility to add and remove AI modules at run-time and
modules presented in Fig. 3 and 4. The fault diagnosis module the dynamic change of their interconnections to create new
is composed by techniques to find abnormal behaviors that workflows.
deviate from normal process conditions to raise warnings For the implementation of the DTO and the Simulation En-
and find root causes for the problem. This algorithm will be vironment, the Simulation and Optimization Environment so-
fed directly with sensor data (when possible and pertinent) lutions provided by CPSwarm will be leveraged and extended.
or transformed data from the field in order to be more Specifically, REPLICA will incorporate the communication
interpretable. Based on the analysis of data streaming, the API based on XMPP and the deployment system, based on
algorithm should indicate if a warning should be sent to the docker and Kubernetes [21]. The use of these technologies will
key personnel to check the system. This algorithm is the first allow to integrate heterogeneous simulators, to simply deploy
front-line of analysis from shop-floor components in order to and run the simulations needed on distributed machines, add
understand machine’s health. and remove at run-time the simulators running different mod-
Additionally, the predictive maintenance module is com- els. More specifically, in the Simulation Environment, consid-
posed of 1) a component failure prediction in the future ering that the solutions proposed in CPSwarm was integrating
(e.g. 48h and which maintenance action should take place); only ROS based simulators, new types of simulators, e.g.,
2) Optimization module for scheduling future maintenance java based simulators, will be included during the RECLAIM
actions based on the existing scheduling; 3) Simulation module project. For this scope, a specific SM will be developed for the
that aims at assessing the impact of changes in the machine required simulators and the API will be refactored to support
and shop-floor [17]. The main idea of this method is to predict also these new simulators. Beside the SM, also docker contain-
what kind of maintenance and when it will be required based ers to easily deploy such simulators will be created. Instead,
on the failing component in the machine. With this, it will be for the implementation of the DTO, the authors have defined
possible to understand what changes need to be done in order that the SOO implemented in CPSwarm will be completely
to compensate the downtime of the failing machine. refactored and extended to support the functionalities required
As can be seen from Figure 3, the implementation already by REPLICA. Additionally, only some of the functionalities
follows a block based approach which allows a better flexibil- of the SOO will be leveraged, extending them to support
ity once building the required data workflows among models. data storage and data analysis features. Finally the DTO will
For this particular case, the Dynamic INtelligent Architecture provide a set of API based on some standard technologies,
for Software MOdular REconfiguration (DINASORE) [18] e.g., Message Queue Telemetry Transport (MQTT), which will
platform was used, which is a run-time environment devel- allow to collect data to be used by the algorithms and to keep
oped in python language for the International Electrotech- the models updated and synchronized with the physical world.
nical Commission (IEC) 61499 standard [19] and integrated Thanks to these API, the DTO will be able to collect data
with the Eclipse based Framework for Distrubeted Industrial from heterogeneous devices that, in the RECLAIM platform,
Automation and Control (4DIAC) Integrated Development are integrated through the IoT Gateway (see Section II). Also
Environment (IDE) [20]. Moreover, this implementation does in this case, it will be possible to add and remove devices
not only allow for the orchestration of models, but also the at run-time, without the need to restart the system. The new
plug&play of such models in a distributed system, where devices can be immediately used by the solution developed,
software can be reconfigured on-demand. This supports both just after they have registered themselves.
completely cloud or mixed edge/cloud systems, depending on Finally, for the implementation of the GUI included in the
the required application and the number of machines available architecture, the presented modules are in different phases
for execution. Finally, since DINASORE is implemented in of development. Specifically, for the Configuration GUI, the
python, the state of the art implementations of AI can be authors have not yet chosen how to implement it and different
promptly used. solutions will be evaluated, keeping in consideration a thor-
For the implementation of the AI Engine that interconnects ough integration with the rest of the platform. Instead, for the
the AI modules, the authors have already evaluated several Output/Monitor GUI, for the monitoring and assessment of the
solutions. One is the possibility to run the modules in a results of the algorithms, a simple implementation based on
docker environment, making them read and write from text the work done in CPSwarm is already available. Specifically,
files located in specific folders and then interconnect them this solution is based on Thingsboard, which has been used
through a software that allows to handle the workflow, e.g., to develop two different GUI: one for process monitoring and
Node-red or NiFi. Another evaluated solution is the possibility another one for the assessment of results. The first one shows
to use Acumos AI to implement the AI environment; in this live data in a chart and allows the monitoring of the process;
59
Fig. 3. Fault Diagnosis algorithm.
exploitation of fault diagnosis and predictive maintenance
techniques based on the use of digital twin will increase
the efficiency of the maintenance activity with respect to the
performance obtained with the traditional methods based on a
fixed schedule and a simple telemetry analysis.
The flexibility and adaptability of REPLICA can be well
demonstrated both during the system setup in the industrial
site and in case of replacement of one machine in the Wood-
working Production Line.
In the first case, when the platform is going to be deployed,
the data collected by the Adaptive Sensorial Network and a
set of AI algorithms for fault diagnosis and predictive main-
tenance developed by different analysts have to be integrated.
Furthermore, to allow the simulation of different operative
Fig. 4. Predictive Maintenance algorithm [17]. scenarios, the realistic models of different machines have to
be imported and executed in one simulator.
Using REPLICA, the effort to make all these components
the latter one, instead, shows the data in a table, where it can be provided by heterogeneous vendors working together is signif-
sorted by column (one column for each data). These GUIs have icantly reduced, allowing their integration by simply exposing
been used in this first implementation, but the possibility to their inputs/outputs through the REPLICA defined interfaces.
enhance or completely replace them with something different, In particular, for the machine simulation, if the simulator used
based on the requirements of the solution proposed, will be to execute it is already supported by REPLICA, no further
evaluated in the future. developments are needed; otherwise only the SM for that
simulator needs to be developed to allow its integration with
VI. RECLAIM USE CASE the rest of the solution. The same advantage can be considered
The aim of this section is to present a possible application also for the AI algorithms: if the ones already integrated
of the solution presented in this paper, focusing on the predic- in the AI environment are suitable for the specific case, no
tive maintenance and refurbishment of a large Woodworking developments are necessary and the platform should be just
Production Line. The main objective of such use case will be configured to enable the correct flow of data among different
to show the benefits of the adoption of advanced maintenance components. Otherwise, to integrate a new algorithms, the only
strategies in a large scale industrial scenario. requirement is to implement the inputs/outputs API defined in
The selected scenario presents different challenges: firstly, REPLICA.
the need to integrate in a single environment both heteroge- Once all the components are connected, the AI modules can
neous data collected by installed sensors at the shop-floor be trained using the data coming from the Adaptive Sensorial
and AI modules; together with the realistic models of the Network (as it is usually done), but also with data produced
machines, enabling and easing the construction of a shop- by the simulated machines. The integration of this secondary
floor’s digital twin. Moreover, the proposed solution will have source of data not only allows to speed up the training process
to optimize the use of a large industrial equipment providing (more data available means less time to learn) but also to add
novel machine learning solutions able to monitor the current the possibility of using data that are generally more difficult to
system status and predict possible failures. In particular, the collect, such as the one associated with specific failures that,
60
for obvious reasons, are not so common in a real industrial of the workflows needed for fault diagnosis and predictive
plant. maintenance, adding/removing/replacing entities to reflect the
The advantages of the REPLICA solution can be further situation of the components available in the system.
demonstrated taking into consideration the scenario of a In this work, the authors introduced the details of the
machines part replacement in the production line which is first implementation of the proposed architecture; since the
already monitored by the system. In this case, the administrator development currently is only in a preliminary phase, Section
of the platform needs to update the components used for V presents the implementations of the modules that were
the fault diagnosis and predictive maintenance to reflect the evolved from previous EU projects’ outcomes. Instead, for
new situation on the field. In a traditional system, this is a the new components only some initial design choices are
process that requires a complete shutdown of the system in presented. In particular, the AI environment and the DTO have
order to setup and reconfigure it; instead, by using REPLICA been just designed and will be developed in next phases of the
the process is fast and mainly automatic. Indeed, for the project, while the Simulation Environment is already available
replacement of the simulation models, this can be done just and only some SMs for new simulators will be developed.
removing the old simulator and instantiating a new one with In the same way, a dashboard for monitoring and results
the updated model, taking advantage of the integration of assessment is ready, while the GUI for configuration has still
the solution with Kubernetes. Once the updated simulator to be implemented.
is instantiated, the same process can be applied to the AI Finally, the authors have provided in Section VI two use
algorithms, which can replace the previous ones. All these cases based on one realistic industrial scenario, which show
updates can be done without the need to interrupt the execution the advantages of using the proposed solution to apply fault
of the system. Obviously, if this change requires the integration diagnosis and predictive maintenance techniques based on
of some new AI modules or the development of a new SM, digital twin.
these have to be implemented in advance. Also the workflow
of the components need to be updated to interconnect the new ACKNOWLEDGMENT
ones, and can be done simply by using the tools provided The work presented here was part of the project
by AI engine and DTO. These will automatically update the ”RECLAIM- RE-manufaCturing and Refurbishment LArge
workflow to reflect the status of the components available in Industrial equipMent” and received funding from the European
the system and that allow the administrator to easily create the Union’s Horizon 2020 research and innovation programme
new workflows. Finally as for the previous use-case, REPLICA under grant agreement No 869884.
can also be used to speed up the training time needed to have
the new algorithms ready to be used, supporting the use of R EFERENCES
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