=Paper=
{{Paper
|id=Vol-3335/SIoT_paper1
|storemode=property
|title=Semantic Models and Machine Learning Approach in
CPS : a Survey
|pdfUrl=https://ceur-ws.org/Vol-3335/SIoT_Paper1.pdf
|volume=Vol-3335
|authors=Hafidi Mohamed Madani,Meriem Djezzar,Hemam Mounir,Ahmed Seghir Zianou,Moufida Maimour
}}
==Semantic Models and Machine Learning Approach in
CPS : a Survey ==
https://ceur-ws.org/Vol-3335/SIoT_Paper1.pdf
Semantic Models and Machine Learning Approach in
CPS :A Survey⋆
Hafidi Mohamed Madani1,2,∗,† , Meriem Djezzar1,3 , Hemam Mounir1,2,∗,† ,
Ahmed Seghir ZIANOU1,2 and Moufida MAIMOUR4,∗,†
1
University of , Khenchela, Algeria
2
ICOSI Laboratory, Khenchela, Algeria
3
LIRE Laboratory, Constantine, Algeria
4
Université de Lorraine, CNRS, CRAN, F-54000 Nancy, France
Abstract
The amazing growth and advancements reached in information and communication technologies in
recent years allow easily the integration of intelligent components and systems into the traditional
manufacturing industry. Enabling new challenges and applications in Industry 4.0 (I4.0) new systems.
Cyber Physical Systems (CPSs) are a new generation of systems composed of a set of collaborative
cyber and physical components with computation capabilities, generating and exchanging data in a loop
between digital and physical worlds in a highly interconnected network. These enormous large amounts
of data produced in or between CPSs are heterogeneous in terms of format and type due to different
data sources, which leads to errors and malfunction in these systems due to the lack of interoperability
between their components. As a result, sharing and exchanging data in CPSs is a challenging task
to do. On other hand, modeling digital systems that reflect the current state of the physical entities
and their behavior is a complex task to achieve. Especially, when communicating and processing data
in real time to extract useful information. To overcome these challenges, semantic data models and
knowledge representation when applied with Machine Learning (ML) techniques can enable solutions
to interoperability problems in CPS, making it possible to mirror the physical reality and monitor it
through cyberspace without misinterpretation and miscommunication in the system.
This paper aims to provide a survey on the state of the art of available solutions to the semantic
interoperability problem in CPS, integrating semantic models, ML, or both technologies combined in a
reference architecture to achieve visionary Interoperable CPSs.
Keywords
Interoperability, Cyber Physical Systems, Machine Learning, Digital Twin, Semantic models, Ontology
SIoT-2022: International Workshop on Semantic IoT (SIoT-2022), Co-located with the KGSWC-2022, November 21-23,
2022, Madrid, Spain.
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open hafidi.mohamedmadani@univ-khenchela.dz (H. M. Madani); meriem.djezzar@univ-khenchela.dz (M. Djezzar);
mounir.hemam@univ-khenchela.dz (H. Mounir); zianou.ahmed.saghir@univ-khenchela.dz (A. S. ZIANOU);
moufida.maimour@univ-lorraine.fr (M. MAIMOUR)
Orcid 0000-0003-2431-4816 (H. M. Madani)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
1. Introduction
The need to increase the product effectiveness, quality, and customization of artificial products
made humans develop and produce new technologies suitable for the elaboration of production
processes that humans are experiencing today [1]. Since the 1980s, and with the third industrial
revolution, the integration of Artificial Intelligence (AI) and manufacturing made the rise of
intelligent manufacturing [2]. introducing new technologies such as robots, Computer Numeric
Control (CNC) machines, and industrial and electronic automation [3].
As AI evolves, smart technologies such as the IoT, semantics and data models, Machine
Learning (ML) algorithms, CPSs, and DTs are taking intelligent manufacturing to a new digital
age known as the fourth industrial revolution, also called Industry 4.0 (I4.0) [4], which is
an initiative that started in Germany to automate production systems efficiently. The main
core of I4.0 is the CPS concept, represented as a multidimensional complex distributed system
integrating a cyber and a dynamic physical world interconnected in an intensive network
providing many functionalities and services such as: real-time sensing, monitoring, and real-
time interaction between entities from both worlds, through the collaboration of computing,
communication and control [5].
The main focus of CPS is to network devices of I4.0 by handling the interaction between
physical reality and computing infrastructures with the use of a communication interface. DT is
the virtual representation of a physical component, it mirrors the status and the activity of the
physical entity by collecting data from the physical environment using IoT devices and sending
it to software systems in order to be analyzed. AI-ML techniques are one of the most promising
and advanced technologies used to create and deploy DTs that mimic the behavior of physical
components.
With the use of communication technologies, DTs and IoT. CPS can be deployed and imple-
mented with respect to the vision of I4.0. But, the massive number of communication between
interconnected physical components and digital systems in Cyber Physical Systems (CPSs)
implies a huge exchange of data, mostly generated, sensed, and shared from different physical
and digital entities in an autonomous way which results in heterogeneity and interoperability
problems, making it hard and barely impossible to implement this new generation of systems
in a full scale without solving and filling the requirements of interoperability and heterogeneity
in communication.
Semantic data models technologies were proposed and used across academia in order to solve
semantic interoperability problems faced at the deployment of CPSs, combined with the use of
ML techniques to model and mirror the physical components of the system in the digital space.
This article is organized as follows. Section 2 provides a brief presentation on I4.0 and CPS,
listing their key concepts and technologies, and focusing on the CPS reference architectures.
Section 3 outlines different CPS proposed architectures and solutions integrating semantic
models and ML algorithms and performs a brief discussion for each solution. Section 4 concludes
with open issues and future direction for researchers.
2. Background
This section covers the different necessary concepts related to the topic to better understand the
proposed survey. It presents a brief definition of the CPS concept, going through its most known
reference architectures in academia, and then a comparison between reference architectures is
discussed. Finally, related concepts of I4.0 and semantic heterogeneity are defined.
2.1. Cyber Physical Systems
The CPS concept was presented for the first time in 2006 by Helen Gill from the National Science
Foundation (NSF) in United States [6]. The concept was introduced as a prospective shift
towards the next generation of engineered systems using future networking and information
technology (NIT) [7]. Many funding programs were created to promote and accelerate the
evolution in this field, CPS-VO is a US organization created to link between CPS professionals in
academia, government, and industry all around the world [7]. Another foundation was created
in Europe named ARTEMIS, its main goal is to develop and accelerate research on the CPS
concept, by creating a partnership between European counties and Industry companies with
the goal to achieve a smart and physical aware world connecting all machines, objects, and
systems in a network with the use of digital information around them to communicate and
collaborate together [7].
Cyber Physical Systems (CPSs) were defined differently by the scientific community. In [8],
CPS is defined as: “integration of computation with physical processes”.
[9] describes CPS as: “physical and engineered systems, whose operations are monitored,
coordinated, controlled, and integrated by a computing and communicating core”.
From Gill’s perspective [10], CPS are: “physical, biological, and engineered systems whose
operations are integrated, monitored, and/or controlled by a computational core. Components
are networked at every scale. Computing is deeply embedded into every physical component,
possibly even into materials. The computational core is an embedded system, usually demands
a real-time response, and is most often distributed”.
CPS is defined in a generic way as software networks of computer systems and physical
processes, with feedback loops, used to monitor and control the physical process [6]. CPS
links each physic component to its DT, enabling knowledge sharing and fast decision-making.
through the integration of computing, communication, and control, making it possible to have
an interactive industrial environment [11].
Unlike embedded systems, CPS focus on the interaction between various digital systems
and their physical twins, by handling with a control unit sensors and actuators that influence
the physical space, and process the data produced and exchanged through a communication
interface [11].
An important characteristic of CPS is the ability to send and obtain information and services
from different devices to other systems autonomously. So it is necessary to ensure its reliability,
efficiency, and security in communication [12]. That is why one of the main requirements of
I4.0 is achieving high interoperability between CPS components.
2.2. IoT, Industry 4.0 and Cyber Physical Systems
Although CPS was presented in 2006, it became a popular trend due to the advancement in
information technology and IoT, which play a big role in collecting real-time data from the
physical world using sensor technology and sharing it with the digital world. The physical-
to-digital connection uses communication technology that enables transmitting data of the
physical components to its virtual twin, by storing it in databases, with the use of WIFI and
cellular networks and their protocols …[13]. The virtual twin converts the data stored using
information and data technologies such as knowledge representation and reasoning to extract
explicit-implicit information and use it to mirror the functioning of the physical twin, which
makes it possible to monitor and predict the status of the physical environment from the virtual
world [14].
The digital-to-physical connection is represented by the information circulating from the
virtual to the physical world, this information may influence the state of the physical components
by changing its parameters or by executing a task to achieve a certain goal (prognostic, diagnosis,
optimization). Both data and Information circulating between the physical and digital twins are
stored in historical databases in order to be processed and analyzed to take final decisions about
optimization, prognostic and diagnosis with the use of different machine learning algorithms,
and semantic web technologies with reasoning [14].
Many benefits can be obtained from implementing IoT and CPS in Industry 4.0, like achieving
better use and performance of assets, minimization of the time and cost of producing assets,
optimization of the product life cycle, faster decision making, improving mass customization
and production in manufacturing [15, 16], monitoring and connecting several manufacturing
machines in a smart environment (software networks) [17].
Despite all the advantages, implementing IoT combined with CPSs in the vision of Industry 4.0
stays a complex mission because of the huge number of different architectures and approaches
of digitalization and networking adopted by enterprises that makes communication and systems
interoperability problems rise [18].
2.3. 5C Architecture
The 5C Architecture of CPS was proposed by Lee in [20]. It consists of a unified 5-level
architecture that serves as a step-by-step guide for developing and implementing CPS in
manufacturing. It is considered a well-known reference data-driven model widely adopted in
developing CPS since it focuses on adopting and defining the data flow in these systems from
the initial data acquisition, to analytics, until the decision making, as shown in Figure 1. The
different levels of the architecture are outlined in Table 1.
2.4. RAMI 4.0
RAMI 4.0 stands for Reference Architectural Model and it consists of a three-dimensional model
that describes all aspects of I4.0 space [21]. In this way, complex interrelations can be broken
down into smaller and simpler clusters. The RAMI 4.0 is composed of:
• The “Hierarchy Levels” axis: on the right horizontal axis in Figure 2 shows the hierarchy
levels from IEC 62264 and IEC 61512, the international standards series for enterprise IT
Figure 1: 5C Architecture of CPS adapted from [19]
Table 1
Different levels of 5C architecture.
5C Level Role
Smart Connection This level is responsible for data acquisition from different interconnected physical
world components with effective management and transfer to the central server.
Physical component identification and specification are required.
Data to Information This level manages the conversion of data collected from the previous step to
Conversion information. This step brings self-awareness to machines.
Cyber This level uses inferred information from the previous step to create Digital Twins
(virtual representation) of physical components of the real world. It is considered
as the center that guarantees communication between physical assets since all
inferred information of collected data is sent to it.
Cognition This level uses information gathered to acquire proper knowledge to monitor the
system and make prognostics for failure prediction and maintenance optimization.
Configuration at this level, operations are sent as feedback from cyberspace to the physical world
to control machines, making the physical space self-configure and self-adaptive
and control systems. These hierarchy levels (Enterprise, Work Centers, Station, Control
Device) represent the different functionalities within factories or facilities. Other layers
(product, field device) were added to the hierarchy levels to support the representation
of the I4.0 space. The top layer labeled “connected world” serves to connect and reach
external partners through service networks.
• The “Life Cycle and Value Stream” axis: The left horizontal axis represents the life cycle
of facilities and products, based on IEC 62890 for life-cycle management. A distinction is
made between the two levels “types” and “instances”. When the product is in design and
development phase. We are in the “type” level. When designing the actual product have
been completed and production starts, we are in the “instance” level.
• The “6 Layers” axis: represented by the vertical axis serves to describe physical ma-
chines and objects from real world: The first layer labeled “Asset” consists of defining
the physical objects and their properties, such as parts, documents, diagrams, humans,
etc…. The second layer named “integration” maps the physical objects to the digital world
components, it serves as a link between physical and digital worlds where transforma-
tions and easy processing of information happens. The third layer “Communication” is
responsible for providing standardized communication between the integration layer
and the information layer, the standardization uses a unified data format that serves to
represent data in an organized way in the information layer, and to process and transform
the data in the integration layer so it can link physical and digital worlds together. The
fourth layer “Information Layer” has the role to store and hold data in an organized way,
its main purpose is to provide relevant information about products from the data stored
(such as statistics on the number of sales, and the number of objects produced). It can also
give the machines used in a production of a product or information about customers and
their feedback…etc. The “functional” layer has the goal to take actions, coordinate, and
select components to proceed with tasks, it involves various activities that can be made
in the system like authentication, user inputs, remote access…etc. The last layer named
“business layer” contains the business strategy, business environment, and management.
It deals with several business activities in order to achieve the business plan model.
2.5. IIRA
The Industrial Internet Reference Architecture (IIRA) is an open cross-industry architecture
developed by IIC based on IIoT standards [23], emphasizing interoperability among industries
such as manufacturing, energy, healthcare, and transportation. This model is organized in five
Viewpoints [24] presented as layers in IIRA, each layer is linked to certain functionalities:
• Business layer: Functions that enable end-to-end tasks in an industrial system (e.g.,
enterprise resource planning, life cycle management, planning, and scheduling);
• Application layer: Functions that enable business operations by implementing application
logics (e.g., activity/workflows, application programming interface, user interface).
• Information layer: Functions that handle data gathering and deployment (e.g., data
collection and storage, semantics, quality processing).
• Operations layer: Functions that enable operations related to component monitoring and
diagnosis throughout their life cycle (e.g., provisioning and deployment, optimization).
• Control layer: Functions that enable the control of an industrial system (sensing and
actuation, communication, abstraction, digitalization, analytics, asset management).
2.6. Comparison between Architectures
This subsection consists of analyzing the similarities and differences between the architecture
reference models described in previous subsections, highlighting their main goals and relations
Figure 2: RAMI 4.0 Architecture adapted from [22]
that make them interoperable with each other. All these architecture reference models define
and represent CPS concepts, but their proposals lie in targeting different goals [24].
At first, the 5C architecture targets modeling and describing assets data collection and
processing, mainly found in small smart industries and IoT environments. It is considered the
first architecture published in the literature and it is adapted only for horizontal integration.
RAMI 4.0 was proposed to describe and model CPS systems in the I4.0 scenario. It focuses
on defining manufacturing assets operations and it focuses on describing in detail CPS design,
control, communication, and business by integrating the value chain company and product
life-cycle.
With IIoT proposal as a highlight, IIRA is based on the ISO/IEC/ IEEE 42010 and describes
the development plan to create the IIoT system. It deeply focuses on the IIoT system as a core
concept in all sectors, such as product life-cycle from design to maintenance and control[24].
As we can see from the architecture reference models, several layers from RAMI 4.0 archi-
tecture are similar in functionalities with levels in the 5C and IIRA architectures. These layers
and levels can be mapped together ensuring possible interoperability establishment between
different architectures.
2.7. Semantic Models
Semantic models have been considered an important technology to achieve interoperability
between different systems [25]. They have the power to model and describe the properties and
relationships between concepts and entities. One of the most used semantic models in research
is ontology.
An ontology is defined as an ‘explicit specification of a conceptualization of a domain’ [26]. It
is used to represent in a generic way a domain knowledge, by encoding it in the form of axioms,
natural language labels, synonyms, definitions, and other types of annotation properties making
it possible to achieve an agreed understanding between applications [27]. Ontology itself is
relatively complex concept that requires a special set of expertise to involve and maintain it
[28]. Most of the ontologies are encoded using the Ontology Web Language (OWL) since it’s
more expressive than other ontology languages like Resource Description Framework Schema
(RDFS). OWL is a part of the Semantic Web stack and it is based on Description Logic [29, 30].
2.7.1. RDF
RDF is a model for encoding semantic relationships between items of data so that these rela-
tionships can be interpreted computationally. It is considered the primary foundation for the
Semantic Web.
2.7.2. RDFS
RDFS is a set of classes with certain properties using the RDF extensible knowledge represen-
tation data model, providing basic elements for the description of ontologies. It uses various
forms of RDF vocabularies, intended to structure RDF resources.
2.7.3. OWL
OWL is a semantic markup language designed to represent complex knowledge as concepts and
their relationships. It is used to publish and share ontologies. It is developed as a vocabulary
extension of RDF. [31]
2.7.4. Semantic Heterogeneity
The semantic heterogeneity concept does not have a unique definition [32]. Several definitions
that provide a certain degree of understanding of the term were found in the literature :
• According to Merriam-Webster dictionary, semantic heterogeneity is defined as a quality
or a state of being made up of parts that are different — related to the meanings of words
and phrases.
• From [33], the term is presented as differences in the meaning and use of data that make it
difficult to identify the various relationships that exist between similar or related objects
in different components.
• Semantic heterogeneity can be defined also as differences in the real-world interpretation
of context, meaning, and use of data [34].
2.8. Digital Twin
DT concept refers to the virtual representation of a physical entity, including all its properties
and functioning. the term was first coined in 2011 by John Vickers. The concept was defined
by its pioneers Grieves and Vickers [35] as ”a set of virtual information constructs that fully
describes a potential or actual physical manufactured product from the micro atomic level to
the macro geometrical level.
At its optimum, any information that could be obtained from inspecting a physically manu-
factured product can be obtained from its DT”. It is mainly used to mirror the physical states
of a manufactured product and to help users predict properties or take action from the digital
space.
We can define Digital Twinning as a process that involves a physical entity, a cyber twin that
mirrors the physical entity and a physical connection (communication interface) used to share
and translate data between them, as shown in Figure 3.
Figure 3: Digital Twining concept schema drawn from [36]
2.9. AI-ML
AI can be defined as the ability to reproduce intelligence reasoning by computer systems for
decision-making purposes.
Machine Learning (ML) is a subset of AI. ML can be defined as the set of techniques that
allow a machine to learn, solve or perform a task without having to program it explicitly. This
set of techniques concerns the analysis, design, development, and implementation of methods
allowing the machine to follow a systematic process to solve a problem, where it will be difficult
or impossible to do so by a classical algorithmic method [37].
The ability to make a machine learns to perform a task requires a set of data that contains
a lot of information specific to the latter. The techniques of ML analyze and process all these
data in order to extract the knowledge and features found and then apply and reuse them on
new data to solve real-life problems. ML techniques are usually categorized into three methods
namely supervised, unsupervised, and Reinforcement Learning (RL) [37].
2.9.1. Supervised Learning
Supervised learning is employed when the training dataset is labeled. Each sample of data is
associated with the desired result (output). The goal of the ML algorithm is to find a function
that maps the input data to the output. Unseen data can be fed as input to a trained model in
order to predict and map it to the most relevant output.
2.9.2. Unsupervised Learning
Unsupervised learning is used in situations where the training data that contains the different
examples are not labeled in advance. Unsupervised learning consists in partitioning the examples
of the training data into categories based on chosen similarity criteria. It allows the automatic
construction of classes without any intervention, but it requires a good estimation of the number
of classes.
2.9.3. Reinforcement Learning
RL is a framework for solving control tasks and decision-making by building agents that learn
from the environment by interacting with it through trial and error and receiving positive and
negative rewards for correct and incorrect performances respectively as feedback. The ultimate
goal of the agent is to maximize its reward in any given situation.
3. Semantic Models, AI-ML, DT Integration in Cyber-Physical
Systems
In this section, we discuss the current deployments and implementation of CPS developed with
the integration of Semantic models technologies, AI-ML techniques, and DT creation. Table 2
outlines the selected works from academia to be discussed and the technologies integrated into
their deployments.
Table 2
Selected papers and the integrated technologies.
Ref Semantic models AI-ML DTs integration
[38] ! !
[16] ! !
[39] ! !
[40] ! !
[41] ! !
[15] ! !
[42] ! !
[43] ! !
[44] !
[45] ! !
[46] ! !
[47] ! !
[48] !
[49] ! !
[50] !
[51] !
[52] ! !
3.1. Semantic Models Integration in CPS
Semantic models can serve to bridge the gap between different project participants in CPSs by
assuring shared understandable information.
In [38], the authors show how machine-to-machine communication is important for collab-
orative manufacturing automation (smart manufacturing). The article introduced a concept
of semantic aware CPS based on industrial machines that can perform semantic machine-to-
machine communications. The authors proposed a semantic and communication layer on top
of the physical and cyber layers of a CPS. The semantic layer provides logical communication
between CPSs from different organizations without the need to be attached together but acts as
if they were by converting the messages exchanged into semantic expressions. The communica-
tion layer then transfers the semantic expressions between CPSs as web requests using HTTP
network protocols.
Different engineering models are created to illustrate different components and tools in CPSs
where each engineering model uses local terminologies related to its engineering environment.
Semantic models can be used as an integration tool of these different models together by
creating ontologies and aligning them at terminological and instance levels to achieve a good
representative DT of the physical system. Each ontology brings specific semantics about each
physical component aspect and provides it to its DT to fulfill and reach a certain goal.
Semantic models can be classified and differentiated according to the semantics they define
as:
• Geometric model: describes the geometric properties (shape, size) of the physical entity.
• Physical model: describes the different parts or the abilities of the physical entity
(composition, capacity…).
• Behavioral model: refers to the behavior of the physical entity communicating with
other entities.
• Rule Model: defines the relation between domain knowledge concepts (constraints,
associations, deduction, negation…).
• Process Model: describes the underlying process and function in which the physical
entity takes part in the CPS.
In the operational phase of CPSs. The DT receives collected data from sensing the physical
environment. This collected data comes from different components and describes different
properties. Semantic models are used to describe and represent the collected data appropriately,
making it possible to visualize and monitor dynamic systems.
A semantic model based on an extension of IoT-Lite ontology is proposed in [16] to represent
and integrate digital twins of devices in an IIoT system. The semantic model focuses on describ-
ing data information of the physical abilities collected from IoT devices and the relationships
between them in a high-level form so it can be represented and interpreted by digital twins of
the IoT system.
The semantic model offers the ability to show information to end users in a better manner.
Moreover, semantic models can extract implicit information about physical objects by reasoning
about the relations between objects in the knowledge base. This kind of information can help
to detect early faults and error occurrences [53]. For example, a DT for part assembly is created
based on an ontology that describes geometrical information about parts and constraints in
assembly units [15], the expressiveness of the ontology made it possible to reason and infer
relations between concepts of the domain knowledge and define assembly requirements between
parts.
In Smart manufacturing, [39] proposed a 4-layer architecture based on ontology to manage
and reconfigure resources in smart manufacturing. The ontology describes manufacturing
resources and their properties using OWL. A rule base model is used to reason about manufac-
turing resources ontology and to reconfigure their status. A reconfiguration of an intelligent
manipulator is implemented as a use case to show the feasibility of this architecture.
Another research in the assembly field [40] presented an ontology-based model of a DT for
assembly workshop. The ontology of the DT assembly workshop is built to describe the objects,
attributes, and relationships that exist in the assembly workshop and participate in the assembly
behaviors. A model of the assembly process is defined as an occurrence of several events in a
range of time, a representation of event ontology is presented as an event-oriented description
logic to act as a logical base of event ontology language. The ontology of the DT assembly
workshop with the assembly process model permits mirroring and monitoring the assembly
workshop using the event ontology language.
The study in [41] presented a DT modeling architecture for manufacturing processes using
multi-agent systems, Material– Process–Functions-Quality (MPFQ) model, and semantic models
to manage knowledge. Semantic models were used to properly define the knowledge and help
multi-agent systems to better interact and interpret shared information, enabling semantic
interoperability. Another study [42] discussed the challenges faced in dealing with DT data
management (data variety, data mining) and their influence on DT dynamics, it proposes a
novel concept of DT ontology model and methodology to address these data management
challenges. The DT ontology model models the conceptual knowledge of the DT domain. Using
the proposed methodology, such domain knowledge is transformed into a minimum data model
structure to map, query, and manage databases for DT applications. The research is tested
using a case study based on Condition Based Monitoring (CBM). From another perspective
of modeling approaches, the research in [43] proposes a semantic modeling approach based
on high-level architecture and GOPPRR for DT integration. The semantic modeling approach
serves as a tool to create DT ontology that describes different heterogeneous properties of the
physical twin to the digital model.
3.2. ML and DT integration in CPS
ML techniques are a key enabler technology in CPSs, they can be applied in both the engineering
and operational phase.
DT can mirror and replicate any physical object in cyberspace, it provides also a feedback
mechanism to control and monitor the physical object. ML algorithms are known for their
high performance in decision-making applications. In monitoring CPSs, anomaly detection is a
required task to control the physical object’s state and its behavior.
In [45], ML techniques were used to detect anomalies in CPSs. A novel approach named
Anomaly DeTection with digiTAl twIN (ATTAIN) is presented in this research paper. A Timed
Automation Machine (TAM) is built to represent the DT of the CPS. Generative Adversarial
Network (GAN) techniques are used to detect anomalies in the CPS. A generator model is
used to capture the characteristics and features of the input data and learn to generate realistic
unlabeled samples with the same features. TAM labels the samples produced by the generator
and feed them to a discriminator with real labeled samples. The discriminator is trained to
distinguish normal data from anomalous data. The use of DT with ML in this approach gave
better results in anomaly detection compared to approaches not using DT.
ML techniques are showing impressive performance at detecting cyber-security attacks
in CPSs, the research in [44] discusses and implements a comparative study on the use of
supervised ML techniques to detect cyber-attacks in CPSs. A case study using a dataset of a
secure water treatment plant is selected to perform cyber attack detection using ML techniques,
results showed impressive accuracy on the trained dataset.
Sensor data collected from the environment and the machines can be used also to detect
failure and errors by training a ML model from historical diagnosis data of the system or from
historical machine data to predict failures and errors in the machines and the system. But,
training an ML model with unbalanced or insufficient data can lead to errors when the model is
used with real-world data. The performance of the ML model depends on the quality of training
data and its volume. In [46]. An architecture that uses a discrete physics-based computational
model with ML techniques to create a DT for investigating several damaged structure scenarios
is presented. A ML classifier that represents the DT of the damaged structure is trained with
data taken and generated from a stochastic computational model that simulates the damaged
structure scenarios, the ML classifier learns to detect damaged structures and serves to warn the
user of the location of the damage. The DT is then connected to the physical entity to enable the
use of real-time decision-making. A use case of a wind turbine is modeled with physics-based
models and data generated from it was used to train a ML classifier to detect damages in the
turbine structure. Results showed a low accuracy in detecting damages.
In [47], a study discusses the creation of a DT with the use of the petrochemical industrial IoT,
ML connected in a loop with the physical factory to exchange information in real-time, realizing
production control optimization. This approach optimizes DT models by applying real-time big
data analysis with ML, it supports petrochemical processes and other manufacturing systems to
dynamically adapt to the changes happening in the environment, taking into account time lags
between time series data and reduction of data dimensionality. Several ML algorithms were
tested and trained using data gathered from the industrial IoT system. A case study in a real
petrochemical factory was used to examine the effectiveness of this approach. Moreover, the
authors in [48] presented a compositional falsification framework for Signal Temporal Logic
(STL) specifications against CPS-ML models based on a decomposition between the analysis
of ML components and the system containing them. A ML analyzer was developed that can
abstract feature spaces, and approximate ML classifiers, providing some miss-classified feature
vectors to be used in the falsification process. A case study of autonomous driving cars is
implemented, the proposed framework using DNN showed effective results. In another context,
a DT architecture reference model using the state machine technique to design cloud-based CPSs
is proposed [49]. Every physical thing is represented by a cloud-based DT of seven elements
(sensors, actuators, functions, events, data storage, network, power unit). Sensing or actuating
the physical thing is considered an event. All the data gathered by the smart things are stored
at different levels of storage from mobile stationary to the cloud-based data center. Bayesian
networks and fuzzy logic were used in this article to create a system that selects system modes
of interaction based on event-triggered and physical things status.
In [50], The authors investigated and tested the Inductive Conformal Prediction (ICP) frame-
work in CPS with ML components for assurance monitoring in real time. ICP framework
provides predictions with well-calibrated confidence, these predictions are combined with a
monitor that ensures a small error rate and limits the number of high-dimensional inputs at
Deep Neural Network (DNN) model in cases where an accurate prediction cannot be made.
Tests were made on two different datasets, different neural network architectures were used
and different non-conformity functions were implemented. Results showed that the proposed
architecture of using the ICP framework combined with DNN model can minimize the number
of alarms due to predictions with multiple classes.
In Task learning, AI-ML algorithms such as RL can provide generalized task policy by training
an agent to learn the task through different knowledge domains bases demonstrating the task.
The authors in [51] proposed a neural network as a control strategy for Connected Vehicles
(CV), the neural network model adjusts the speed of the following vehicle taking into account
the distance, deceleration, and speed of the leading vehicle.
In [52], the authors proposed an algorithm based on RL that uses data from the manufacturing
system to fix errors and differences in data while representing the DT of the CPS. The DT acts
as the agent of the RL algorithm to ensure minimal policy. The algorithm proposed was tested
in sheet metal assembly.
4. Conclusion
Given the different architecture proposals that integrate semantic models to ensure interoper-
ability in communication between Cyber-Physical spaces and ML techniques to create virtual
replicas of the physical world components, CPSs are a complex concept to design and implement,
semantic models such ontologies can simplify the representation of data in a good manner and
facilitate the construction of a DT of the physical reality.
The use of DT can increase the performance of CPSs effectiveness and simplifies their
monitoring to end users by providing an appropriate digital representation of the physical
system.
The use of ontology makes data management easy and communication easier, data and
information about concepts are well-defined by properties and information flow between
concepts is highlighted with relationships that ensure good communication and interpretation
of all the data in the digital space. Several tasks can be accomplished such as anomaly detection
with the use of an Ontology, a rule base can be used to reason about the ontology and extract
implicit information about the physical system.
ML techniques serve in both controlling and monitoring the CPS. They can be a tool for
constructing a DT by training a model from data gathered or generated from cyber-physical
spaces. The DT-trained model predicts the behavior of the physical system from learning
patterns using historical data gathered.
Generating data from simulation using physics-based computational models and feeding it to
ML models can bring significance to results obtained from these ML models, by analyzing and
mapping the results obtained from the trained model to physics-based parameters.
One of the relevant challenges in implementing ML in CPS is that data generated from the
simulation are not sufficient. The use of a hybrid way to generate samples with the same
features characteristic of real data gathered from reality and similar in distribution is required,
at Last, ML techniques can approximate the behavior of the real system but there will be always
a difference between real data and simulated data from DT.
Ontology modeling with ML techniques can be used to analyze and represent the semantic
mismatch between real data and simulated data by calculating similarities and comparing their
features and distribution. We believe that semantic models, DT, and ML together can serve
as a bridge to create the visionary CPS and ensure knowledge interoperability when all three
technologies are applied in a good way and in a refined architecture.
This survey performed a review of the CPS reference architectures (5C, RAMI 4.0, and IIRA)
with a comparison between them. Furthermore, this article proposed a literature review of CPS
architecture projects and an analysis of the use of semantic models and ML employed in CPS
engineering and implementation.
For future works, combining semantic models and ML techniques together following I4.0
reference architectures design as an inspiration to implement CPS is considered, since it could
bring a high level of integrity to these systems. In addition, an exhaustive and detailed archi-
tecture that takes into account all aspects of designing CPS using ML and Ontology together
must be explored and defined as a unified standard solution to ensure interoperability between
different industrial systems and achieve I4.0 vision.
References
[1] F. Yang, S. Gu, Industry 4.0, a revolution that requires technology and national strategies,
Complex & Intelligent Systems 7 (2021) 1311–1325.
[2] J. Zhou, P. Li, Y. Zhou, B. Wang, J. Zang, L. Meng, Toward new-generation intelligent
manufacturing, Engineering 4 (2018) 11–20.
[3] G. Schuh, T. Potente, R. Varandani, C. Hausberg, B. Fränken, Collaboration moves pro-
ductivity to the next level, Procedia CIRP 17 (2014) 3–8. URL: https://www.sciencedirect.
com/science/article/pii/S2212827114003709. doi:h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j . p r o c i r . 2 0 1 4 .
0 2 . 0 3 7 , variety Management in Manufacturing.
[4] P. Dallasega, E. Rauch, C. Linder, Industry 4.0 as an enabler of proximity for construction
supply chains: A systematic literature review, Computers in industry 99 (2018) 205–225.
[5] F. Tao, Q. Qi, L. Wang, A. Nee, Digital twins and cyber–physical systems toward smart
manufacturing and industry 4.0: Correlation and comparison, Engineering 5 (2019)
653–661.
[6] E. A. Lee, S. A. Seshia, Introduction to embedded systems: A cyber-physical systems
approach, Mit Press, 2016.
[7] V. Gunes, S. Peter, T. Givargis, F. Vahid, A survey on concepts, applications, and challenges
in cyber-physical systems, KSII Transactions on Internet and Information Systems (TIIS) 8
(2014) 4242–4268.
[8] E. A. Lee, Cyber physical systems: Design challenges, in: 2008 11th IEEE international
symposium on object and component-oriented real-time distributed computing (ISORC),
IEEE, 2008, pp. 363–369.
[9] R. Rajkumar, I. Lee, L. Sha, J. Stankovic, Cyber-physical systems: the next computing
revolution, in: Design automation conference, IEEE, 2010, pp. 731–736.
[10] H. Gill, A continuing vision: Cyber-physical systems, in: Fourth annual Carnegie Mellon
conference on the electricity industry, 2008.
[11] N. Carvalho, O. Chaim, E. Cazarini, M. Gerolamo, Manufacturing in the fourth industrial
revolution: A positive prospect in sustainable manufacturing, Procedia Manufacturing 21
(2018) 671–678.
[12] G.-J. Cheng, L.-T. Liu, X.-J. Qiang, Y. Liu, Industry 4.0 development and application of
intelligent manufacturing, in: 2016 international conference on information system and
artificial intelligence (ISAI), IEEE, 2016, pp. 407–410.
[13] A. Redelinghuys, A. Basson, K. Kruger, A six-layer digital twin architecture for a manufac-
turing cell, in: International Workshop on Service Orientation in Holonic and Multi-Agent
Manufacturing, Springer, 2018, pp. 412–423.
[14] R. van Dinter, B. Tekinerdogan, C. Catal, Predictive maintenance using digital twins: A
systematic literature review, Information and Software Technology (2022) 107008.
[15] Q. Bao, G. Zhao, Y. Yu, S. Dai, W. Wang, Ontology-based modeling of part digital twin
oriented to assembly, Proceedings of the Institution of Mechanical Engineers, Part B:
Journal of Engineering Manufacture 236 (2022) 16–28.
[16] C. Steinmetz, A. Rettberg, F. G. C. Ribeiro, G. Schroeder, C. E. Pereira, Internet of things
ontology for digital twin in cyber physical systems, in: 2018 VIII Brazilian symposium on
computing systems engineering (SBESC), IEEE, 2018, pp. 154–159.
[17] M. Kherbache, M. Maimour, E. Rondeau, When digital twin meets network softwarization
in the industrial iot: Real-time requirements case study, Sensors 21 (2021) 8194.
[18] Z. Huang, C. Jowers, D. Kent, A. Dehghan-Manshadi, M. S. Dargusch, The implementation
of industry 4.0 in manufacturing: from lean manufacturing to product design, The
International Journal of Advanced Manufacturing Technology 121 (2022) 3351–3367.
[19] R. M. de Salles, F. A. Coda, J. R. Silva, D. J. dos Santos Filho, P. E. Miyagi, F. Junqueira,
Requirements analysis for machine to machine integration within industry 4.0, in: 2018
13th IEEE International Conference on Industry Applications (INDUSCON), IEEE, 2018,
pp. 1237–1243.
[20] J. Lee, B. Bagheri, H.-A. Kao, A cyber-physical systems architecture for industry 4.0-based
manufacturing systems, Manufacturing letters 3 (2015) 18–23.
[21] K. Schweichhart, Reference architectural model industrie 4.0 (rami 4.0), An Introduction.
Available online: https://www. plattform-i40. de I 40 (2016).
[22] M. Hankel, B. Rexroth, The reference architectural model industrie 4.0 (rami 4.0), ZVEI 2
(2015) 4–9.
[23] S.-W. Lin, B. Miller, J. Durand, R. Joshi, P. Didier, A. Chigani, R. Torenbeek, D. Duggal,
R. Martin, G. Bleakley, et al., Industrial internet reference architecture, Industrial Internet
Consortium (IIC), Tech. Rep (2015).
[24] M. Moghaddam, M. N. Cadavid, C. R. Kenley, A. V. Deshmukh, Reference architectures
for smart manufacturing: A critical review, Journal of manufacturing systems 49 (2018)
215–225.
[25] M. Djezzar, M. Hemam, M. Maimour, F. Z. Amara, K. Falek, Z. A. Seghir, An approach for
semantic enrichment of sensor data, in: 2018 3rd International Conference on Pattern
Analysis and Intelligent Systems (PAIS), IEEE, 2018, pp. 1–7.
[26] T. R. Gruber, Toward principles for the design of ontologies used for knowledge sharing?,
International journal of human-computer studies 43 (1995) 907–928.
[27] F. Ortiz-Rodriguez, S. Tiwari, R. Panchal, J. M. Medina-Quintero, R. Barrera, MEXIN:
Multidialectal Ontology Supporting NLP Approach to Improve Government Electronic
Communication with the Mexican Ethnic Groups, 2022, p. 461–463.
[28] A. Nikiforova, S. Tiwari, V. Rovite, J. Klovins, N. Kante, Evaluation and visualization of
healthcare semantic models, Evaluation 323 (2020) 91773–5.
[29] J. Domingue, D. Fensel, J. A. Hendler, Handbook of semantic web technologies, Springer
Science & Business Media, 2011.
[30] M. Hemam, M. Djezzar, Z. Boufaida, Multi-viewpoint ontological representation of com-
posite concepts: a description logics-based approach, International Journal of Intelligent
Information and Database Systems 10 (2017) 51–68.
[31] M. M. Taye, Understanding semantic web and ontologies: Theory and applications, arXiv
preprint arXiv:1006.4567 (2010).
[32] V. Jirkovskỳ , M. Obitko, V. Mařík, Understanding data heterogeneity in the context of
cyber-physical systems integration, IEEE Transactions on Industrial Informatics 13 (2016)
660–667.
[33] J. Hammer, D. McLeod, An approach to resolving semantic heterogeneity in a federation
of autonomous, heterogeneous database systems, International Journal of Intelligent and
Cooperative Information Systems 2 (1993) 51–83.
[34] D. George, Understanding structural and semantic heterogeneity in the context of database
schema integration, Journal of the Department of Computing, UCLAN 4 (2005) 29–44.
[35] M. Grieves, J. Vickers, Digital twin: Mitigating unpredictable, undesirable emergent
behavior in complex systems, in: Transdisciplinary perspectives on complex systems,
Springer, 2017, pp. 85–113.
[36] E. Fontes, Digital twins and model-based battery design, 2019.
[37] M. HAFIDI, Contribution au dépistage intelligent du cancer du sein basé sur la thermogra-
phie médicale (2020).
[38] Y. Lu, M. R. Asghar, Semantic communications between distributed cyber-physical systems
towards collaborative automation for smart manufacturing, Journal of manufacturing
systems 55 (2020) 348–359.
[39] J. Wan, B. Yin, D. Li, A. Celesti, F. Tao, Q. Hua, An ontology-based resource reconfigu-
ration method for manufacturing cyber-physical systems, IEEE/ASME Transactions on
Mechatronics 23 (2018) 2537–2546.
[40] C. Zhang, W. Xu, J. Liu, Z. Liu, Z. Zhou, D. T. Pham, A reconfigurable modeling approach
for digital twin-based manufacturing system, Procedia Cirp 83 (2019) 118–125.
[41] X. Zheng, F. Psarommatis, P. Petrali, C. Turrin, J. Lu, D. Kiritsis, A quality-oriented digital
twin modelling method for manufacturing processes based on a multi-agent architecture,
Procedia Manufacturing 51 (2020) 309–315.
[42] S. Singh, E. Shehab, N. Higgins, K. Fowler, D. Reynolds, J. A. Erkoyuncu, P. Gadd, Data
management for developing digital twin ontology model, Proceedings of the Institution of
Mechanical Engineers, Part B: Journal of Engineering Manufacture 235 (2021) 2323–2337.
[43] H. Li, J. Lu, X. Zheng, G. Wang, D. Kiritsis, Supporting digital twin integration using
semantic modeling and high-level architecture, in: IFIP International Conference on
Advances in Production Management Systems, Springer, 2021, pp. 228–236.
[44] P. Semwal, A. Handa, Cyber-attack detection in cyber-physical systems using supervised
machine learning, in: Handbook of Big Data Analytics and Forensics, Springer, 2022, pp.
131–140.
[45] Q. Xu, S. Ali, T. Yue, Digital twin-based anomaly detection in cyber-physical systems, in:
2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST), IEEE,
2021, pp. 205–216.
[46] T. Ritto, F. Rochinha, Digital twin, physics-based model, and machine learning applied
to damage detection in structures, Mechanical Systems and Signal Processing 155 (2021)
107614.
[47] Q. Min, Y. Lu, Z. Liu, C. Su, B. Wang, Machine learning based digital twin framework for
production optimization in petrochemical industry, International Journal of Information
Management 49 (2019) 502–519.
[48] T. Dreossi, A. Donzé, S. A. Seshia, Compositional falsification of cyber-physical systems
with machine learning components, Journal of Automated Reasoning 63 (2019) 1031–1053.
[49] K. M. Alam, A. El Saddik, C2ps: A digital twin architecture reference model for the
cloud-based cyber-physical systems, IEEE access 5 (2017) 2050–2062.
[50] D. Boursinos, X. Koutsoukos, Assurance monitoring of cyber-physical systems with
machine learning components, arXiv preprint arXiv:2001.05014 (2020).
[51] A. Sargolzaei, C. D. Crane, A. Abbaspour, S. Noei, A machine learning approach for fault
detection in vehicular cyber-physical systems, in: 2016 15th IEEE International Conference
on Machine Learning and Applications (ICMLA), IEEE, 2016, pp. 636–640.
[52] C. Cronrath, A. R. Aderiani, B. Lennartson, Enhancing digital twins through reinforce-
ment learning, in: 2019 IEEE 15th International Conference on Automation Science and
Engineering (CASE), IEEE, 2019, pp. 293–298.
[53] M. Sabou, S. Biffl, A. Einfalt, L. Krammer, W. Kastner, F. J. Ekaputra, Semantics for
cyber-physical systems: A cross-domain perspective, Semantic Web 11 (2020) 115–124.