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Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS.
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Research Letters
A Cyber-Physical Systems architecture for Industry
4.0-based manufacturing systems
Jay Lee, Behrad Bagheri
, Hung-An Kao
NSF Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH, United States
Received 8 October 2014; accepted 2 December 2014
Available online 10 December 2014
Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within
which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber
computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more
efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry
4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture
is proposed as a guideline for implementation of CPS.
Ó2014 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.
Keywords: Cyber-Physical System; Industry 4.0; Health management and prognostics; Time machine
1. Introduction
Cyber-Physical Systems (CPS) is defined as transforma-
tive technologies for managing interconnected systems
between its physical assets and computational capabilities
[1]. With recent developments that have resulted in higher
availability and affordability of sensors, data acquisition
systems and computer networks, the competitive nature
of today’s industry forces more factories to move toward
implementing high-tech methodologies. Consequently, the
ever growing use of sensors and networked machines has
resulted in the continuous generation of high volume data
which is known as Big Data [2,3]. In such an environment,
CPS can be further developed for managing Big Data and
leveraging the interconnectivity of machines to reach the
goal of intelligent, resilient and self-adaptable machines
[4,5]. Furthermore by integrating CPS with production,
logistics and services in the current industrial practices, it
would transform today’s factories into an Industry 4.0 fac-
tory with significant economic potential [6,7]. For instance,
a joint report by the Fraunhofer Institute and the industry
association Bitkom said that German gross value can be
boosted by a cumulative 267 billion euros by 2025 after
introducing Industry 4.0 [8]. A brief comparison between
current and Industry 4.0 factories is presented in Table 1
Since CPS is in the initial stage of development, it is
essential to clearly define the structure and methodology
of CPS as guidelines for its implementation in industry.
To meet such a demand, a unified system framework has
been designed for general applications. Furthermore, cor-
responding algorithms and technologies at each system
layer are also proposed to collaborate with the unified
structure and realize the desired functionalities of the over-
all system for enhanced equipment efficiency, reliability
and product quality.
2213-8463/Ó2014 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.
Corresponding author.
Available online at
Manufacturing Letters 3 (2015) 18–23
2. CPS 5C level architecture
The proposed 5-level CPS structure, namely the 5C
architecture, provides a step-by-step guideline for develop-
ing and deploying a CPS for manufacturing application.
In general, a CPS consists of two main functional compo-
nents: (1) the advanced connectivity that ensures real-time
data acquisition from the physical world and information
feedback from the cyber space; and (2) intelligent data man-
agement, analytics and computational capability that con-
structs the cyber space. However, such requirement is very
abstract and not specific enough for implementation pur-
pose in general. In contrast, the 5C architecture presented
here clearly defines, through a sequential workflow manner,
how to construct a CPS from the initial data acquisition, to
analytics, to the final value creation. As illustrated in Fig. 1,
the detailed 5C architecture is outlined as follows:
2.1. Smart connection
Acquiring accurate and reliable data from machines
and their components is the first step in developing a
Cyber-Physical System application. The data might be
directly measured by sensors or obtained from controller
or enterprise manufacturing systems such as ERP, MES,
SCM and CMM. Two important factors at this level have
to be considered. First, considering various types of data, a
seamless and tether-free method to manage data acquisi-
tion procedure and transferring data to the central server
is required where specific protocols such as MTConnect
[10] and etc. are effectively useful. On the other hand,
selecting proper sensors (type and specification) is the sec-
ond important consideration for the first level.
2.2. Data-to-information conversion
Meaningful information has to be inferred from the data.
Currently, there are several tools and methodologies
available for the data to information conversion level. In
recent years, extensive focus has been applied to develop
these algorithms specifically for prognostics and health
management applications. By calculating health value, esti-
mated remaining useful life and etc., the second level of CPS
architecture brings self-awareness to machines (Fig. 2).
Table 1
Comparison of today’s factory and an Industry 4.0 factory.
Data source Today’s factory Industry 4.0
Attributes Technologies Attributes Technologies
Component Sensor Precision Smart sensors and fault
Degradation monitoring &
remaining useful life prediction
Machine Controller Producibility &
Condition-based monitoring
& diagnostics
Up time with predictive health
Productivity & OEE Lean operations: work and
waste reduction
Worry-free productivity
Fig. 1. 5C architecture for implementation of Cyber-Physical System.
J. Lee et al. / Manufacturing Letters 3 (2015) 18–23 19
2.3. Cyber
The cyber level acts as central information hub in this
architecture. Information is being pushed to it from every
connected machine to form the machines network. Having
massive information gathered, specific analytics have to be
used to extract additional information that provide better
insight over the status of individual machines among the
fleet. These analytics provide machines with self-comparison
ability, where the performance of a single machine can be
compared with and rated among the fleet. On the other
hand, similarities between machine performance and previ-
ous assets (historical information) can be measured to pre-
dict the future behavior of the machinery. In this paper,
we briefly introduce an efficient yet effective methodology
for managing and analyzing information at cyber level (Sec-
tion 3).
2.4. Cognition
Implementing CPS upon this level generates a thorough
knowledge of the monitored system. Proper presentation
of the acquired knowledge to expert users supports the
correct decision to be taken. Since comparative informa-
tion as well as individual machine status is available, deci-
sion on priority of tasks to optimize the maintaining
process can be made. For this level, proper info-graphics
are necessary to completely transfer acquired knowledge
to the users.
2.5. Configuration
The configuration level is the feedback from cyber space
to physical space and acts as supervisory control to make
machines self-configure and self-adaptive. This stage acts
as resilience control system (RCS) to apply the corrective
and preventive decisions, which has been made in cognition
level, to the monitored system.
3. Design of PHM based CPS systems
The extreme advantage of cyber level PHM is the inter-
connection between machine health analytics through a
machine–cyber interface (CPI) at the cyber level, which is
conceptually similar to social networks. Once the cyber-
level infrastructure is in place, machines can register into
the network and exchange information through cyber-
interfaces. At this point, an algorithm has to be established
to track the changes of a machine status, infer additional
knowledge from historical information, apply peer-to-peer
comparison and pass the outputs to the next level. New
methods have to be developed to perform these actions
and generate appropriate results. In this paper, we intro-
duce the time machinethat performs analytics at the
cyber level and consists of three parallel sections as follows.
I. Snapshot collection: As illustrated in Fig. 3, informa-
tion is continuously being pushed to the cyber space
from machines. The role of snapshot collection is to
(DSS )
(CPS )
and Health
Actions to Avoid
Prioritize and
Compon ent s
Fleet of Machines
M #1
M #2
Time- Machine Snapshot s
Adaptive Analysis
Effe ctive Se nsor Sel ectio n
Peer to Peer Monitoring
Fig. 2. Applications and techniques associated with each level of the 5C architecture.
20 J. Lee et al. / Manufacturing Letters 3 (2015) 18–23
manage the incoming data and store the information
in an efficient fashion. Basically, to reduce required
disk space and process power, snapshots of machine
performance, utilization history and maintenance
has to be recorded instead of the whole time-series.
These snapshots are only taken once a significant
change has been made to the status of the monitored
machine. The change can be defined as dramatic var-
iation in machine health value, a maintenance action
or a change in the working regime. During the life
cycle of a machine, these snapshots will be accumu-
lated and used to construct the time-machine history
of the particular asset. This active time-machine
record will be used for peer-to-peer comparison
between assets. Once the asset is failed or replaced,
its relative time-machine record will change status
from active to historical and will be used as similarity
identification and synthesis reference.
II. Similarity identification: In cyber level, due to avail-
ability of information from several machines, the
likelihood of capturing certain failure modes in a
shorter time frame is higher. Therefore, the similarity
identification section has to look back in historical
time machine records to calculate the similarity of
current machine behavior with former assets utiliza-
tion and health. At this stage, different algorithms
can be utilized to perform pattern matching such as
match matrix, trajectory similarity method [11] or
various stochastic methods. Once the patterns are
matched, future behavior of the monitored system
can be predicted more accurately.
III. Synthesis optimized future steps: Predicting remaining
useful life of assets helps to maintain just-in-time
maintenance strategy in manufacturing plant. In
addition, life prediction along with historical time
machine records can be used to improve the asset
Fig. 3. Time machine approach for Cyber-Physical PHM.
J. Lee et al. / Manufacturing Letters 3 (2015) 18–23 21
utilization efficiency based on its current health sta-
tus. Historical utilization patterns of similar asset at
various health stages provide required information
to simulate possible future utilization scenarios and
their outcome for the target asset. Among those sce-
narios, the most efficient and yet productive utiliza-
tion pattern can be implemented for the target asset.
4. Implementation of 5C CPS architecture for factories
Implementing CPS in today’s factories offers several
advantages that can be categorized in three stages of
component, machine and production system that have been
introduced in Table 1. Considering a production line con-
sists of a numerous amount of machine tools, the advanta-
ges of a CPS enabled company at the aforementioned
stages can be observed. At the component stage, once the
sensory data from critical components has been converted
into information, a cyber-twin of each component will be
responsible for capturing time machine records and synthe-
sizing future steps to provide self-awareness and self-
prediction. At the next stage, more advanced machine data,
e.g. controller parameters, would be aggregated to the com-
ponents information to monitor the status and generate the
cyber-twin of each particular machine. These machine twins
in CPS provide the additional self-comparison capability.
Further at the third stage (production system), aggregated
knowledge from components and machine level information
provides self-configurability and self-maintainability to the
factory. This level of knowledge not only guarantees a worry
free and near zero downtime production, but also provides
optimized production planning and inventory management
plans for factory management (Fig. 4).
5. Conclusions
This paper presents a 5C architecture for Cyber-Physical
Systems in Industry 4.0 manufacturing systems. It provides
a viable and practical guideline for manufacturing industry
to implement CPS for better product quality and system
reliability with more intelligent and resilient manufacturing
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Predictive factories: the next transformation. Manuf Leadership
  • Lee J Lapira
Lee J, Lapira E. Predictive factories: the next transformation. Manuf Leadership J 2013.
Improving machine tool interoperability using standardized interface protocols: MTConnect
  • A Vijayaraghavan
  • W Sobel
  • A Fox
  • D Dornfeld
  • P Warndorf
Vijayaraghavan A, Sobel W, Fox A, Dornfeld D, Warndorf P. Improving machine tool interoperability using standardized interface protocols: MTConnect. In: Proceedings of the 2008 international symposium on flexible automation (ISFA), 2008, Atlanta, GA, USA.