ArticlePDF Available

Abstract and Figures

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.
Content may be subject to copyright.
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
Abstract
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
[9].
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.
http://dx.doi.org/10.1016/j.mfglet.2014.12.001
2213-8463/Ó2014 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.
Corresponding author.
www.elsevier.com/locate/mfglet
Available online at www.sciencedirect.com
ScienceDirect
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
detection
Self-aware
Self-predict
Degradation monitoring &
remaining useful life prediction
Machine Controller Producibility &
performance
Condition-based monitoring
& diagnostics
Self-aware
Self-predict
Self-compare
Up time with predictive health
monitoring
Production
system
Networked
system
Productivity & OEE Lean operations: work and
waste reduction
Self-configure
Self-maintain
Self-organize
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
Configure
Cognition
Cyber
Conversion
Connection
Resilient
Control
System
(RCS)
Decision
Support
System
(DSS )
Cyber-
Physical
Systems
(CPS )
Prognostics
and Health
Management
(PHM)
Condition
Based
Monitoring
(CBM)
Actions to Avoid
Prioritize and
Optimize
Decisions
Self-Compare
Self-Aware
Condition
Monitoring
Sensors
Compon ent s
Machines
Fleet of Machines
#3
M #1
M #2
Time- Machine Snapshot s
Supervisory
Control
Adaptive Analysis
Effe ctive Se nsor Sel ectio n
Peer to Peer Monitoring
Required
Actions
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
equipment.
References
[1] Baheti R, Gill H. Cyber-physical systems. Impact Control Technol
2011:1–6.
[2] Lee J, Lapira E, Bagheri B, Kao H. Recent advances and trends in
predictive manufacturing systems in big data environment. Manuf
Letter 2013;38:41.
[3] Shi J, Wan J, Yan H, Suo H. A survey of cyber-physical systems. In:
International conference on wireless communications and signal
processing (WCP), November 2011; p. 1–6.
[4] Krogh BH. Cyber physical systems: the need for new models and
design paradigms. Carnegie Mellon University. p. 1–31. <http://
www.control.lth.se/user/karlerik/Illinois/Cyber-Physical/CPS_pre-
sentation_krogh.ppt/>.
[5] National Institute of Standards and Technology. Workshop report on
foundations for innovation in cyber-physical systems, January 2013
Fig. 4. The flow of data and information in a CPS enabled factory with machine tools in production line based on 5C CPS architecture.
22 J. Lee et al. / Manufacturing Letters 3 (2015) 18–23
<http://www.nist.gov/el/upload/CPS-WorkshopReport-1-30-13-Fi-
nal.pdf/.
[6] Lee J, Lapira E. Predictive factories: the next transformation. Manuf
Leadership J 2013.
[7] Lee J, Lapira E, Yang S, Kao HA. Predictive manufacturing system
trends of nextgeneration productionsystems. In: Proceedings of the 11th
IFAC workshop on intelligent manufacturing systems; 2013. p. 150–6.
[8] Heng S. Industry 4.0: Huge potential for value creation waiting to be
tapped. Deutsche Bank Research. <https://www.dbresearch.com/
servlet/reweb2.ReWEB?document=PROD0000000000335628&rwn-
ode=DBR_INTERNET_EN-PROD$NAVIGATION&rwobj=
ReDisplay.Start.class&rwsite=DBR_INTERNET_EN-PROD/>.
[9] Industry 4.0 in Big Data environment. German Harting Magazine
2013;26:8–10.
[10] 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.
[11] Wang T, Yu J, Siegel D, Lee J. A similarity-based prognostics
approach for remaining useful life estimation of engineered systems.
In: International conference on prognostics and health management
(PHM). IEEE; 2008. p. 1–6.
J. Lee et al. / Manufacturing Letters 3 (2015) 18–23 23
... A process that benefits from Industry 4.0 is smart maintenance: the management of maintenance activities supported by "pervasive digital technologies" (Bokrantz et al., 2020). Here, Industry 4.0 technologies enable machines that are self-aware of their own status and can self-predict their future health (Lee et al., 2015). These capabilities are enabled by the adoption of CPSs: systems which include machinery that understands its own and other's condition through data collection (Thoben et al., 2017;Barbieri and Fantuzzi, 2016). ...
... Before the execution of smart retrofitting activities, it is important to identify which capabilities must be implemented in legacy devices. One potential answer is provided by Lee et al. (2015) in their 5C architecture. They propose a "step-by-step guideline for developing and deploying a CPS architecture for manufacturing applications". ...
Article
Full-text available
The management of industrial maintenance has rapidly evolved in the past few decades. Thanks to the advancements of Cyber-Physical Systems (CPSs) brought by Industry 4.0, the present and future health state of machines can be estimated and, as a result, smart maintenance based on real-time sensor data has emerged. One way to implement CPSs in an enterprise is through smart retrofitting: the integration of new technologies into legacy devices to enable CPS capabilities. Among these capabilities, communication involves the integration, translation and transmission of data from legacy devices to external applications. In this work, XWare (eXperimental middleWare) is proposed as a low-cost middleware for smart maintenance applications that eases the implementation of the communication capability into legacy devices. This implementation is enabled through the integration of various open-source technologies: MQTT, OM2M, and a custom-made library of Python functions. The integration of these components allows sensors placed on different locations to transmit maintenance-related data to a local server through the use of gateways. By methodically designing, implementing and verifying XWare, a scalable, easy to deploy, and open-source middleware for smart retrofitting in maintenance was developed.
... The implementation of manufacture in the concept of Industry 4.0 imposes new requirements on technological equipment that integrates into the cyberspace of the enterprise, which has a multi-level structure. The executive (factory shop) level, where complex industrial equipment operates in the real time and technological processes for the production of products of a necessary quality are implemented in a regulated time [1,2]. ...
Conference Paper
Full-text available
The work is devoted to the study and solution of the problems of safety operation of CNC equipment in "smart" digital manufacture. The principles of ensuring the safety operation of CNC machine mechanisms based on external programmable logic safety controllers are considered. Theoretical aspects of the development and practical solution of Safety PLC for technological equipment operating in automatic mode are presented. The general principle of functioning of the system of checking and ensuring the safety operation of the machine tools mechanisms, built based on a SoftPLC integrated in the CNC system and an external Safety controller made according to the SoftPLC type, is described. As an example, a technical solution to ensure the safe operation of the brake of the vertical axis of a CNC machine is demonstrated. A block diagram of the algorithm of the operation and a software implementation of the Safe Brake Test functional block are presented.
... Para lograr el mejor desempeño de estos sistemas, las empresas manufactureras o de servicios se ven enfrentadas a inmensos retos, entre ellos, a la amplia complejidad de los procesos productivos y las nuevas exigencias de los mercados, con lo cual deben responder con sistemas de producción modernos y enfoques sofisticados de control, para mantener altos niveles de flexibilidad y adaptabilidad. En este sentido, las solicitudes de producción personalizada, con la utilización de recursos de producción heterogéneos, aumentan la diversidad de los sistemas de fabricación, lo que hace que su reconfiguración sea aún más compleja y lenta para atender ágilmente las dinámicas competitivas de los mercados (Lee et al., 2015;Krüger et al., 2017;Nikolakis et al., 2020). ...
Article
Full-text available
Esta investigación tiene como objetivo evaluar la eficiencia en los sistemas productivos de bienes y servicios de las pequeñas y medianas empresas (Pymes) en el Departamento de Bolívar-Colombia. Para este propósito se utilizó la técnica de Análisis Envolvente de Datos (DEA), en la cual se determinó las eficiencias técnicas de las 120 Pymes formalmente registradas en la Cámara de Comercio de Cartagena para los años 2017 a 2020. Se contrasta con otros estudios cuya técnica no paramétrica fue aplicada en sectores productivos similares que, el grupo de pequeñas y medianas empresas evaluadas mostraron resultados análogos en sus procesos operacionales. Se concluye que las Pymes evaluadas presentaron un desempeño productivo exiguo en sus actividades operacionales debido a factores relacionados con el bajo aplacamiento financiero y deficiente gestión de la innovación.
Article
To solve the problem of predictive maintenance for packaging manufacturing, we propose a hybrid model that optimizes the maintenance plan. The model is based on monitoring the state of many components of a multi-position automatic packaging machine and makes it possible to predict their future malfunctions and estimate the remaining service life of the equipment. The effectiveness of the proposed solution is demonstrated with the help of a real industrial multi-position machine for the automatic production of film bags and packaging of paste in them. The methodology is based on the analysis of diagnostic information using an expert system.
Thesis
Dans le cadre de sa quatrième révolution, le monde industriel subit une forte digitalisation dans tous les secteurs d’activité. Les travaux de recherche de cette thèse s’intègrent dans un contexte de transition vers l’industrie du futur, et plus spécifiquement dans les industries d’usinage mécanique. Ces travaux de recherche répondent ainsi à la problématique d’intégration données et connaissances industrielles, comme support aux systèmes d’aide à la décision. L’approche proposée est appliquée au diagnostic de défaillance des machines d’usinage connectées. Cette thèse propose, dans un premier temps, un cadre conceptuel pour la structuration de bases de données et de connaissances hétérogènes, nécessaires pour la mise en place du SAD.Grace à une première fonction de traçabilité, le système capitalise la description des caractéristiques de tous les événements particuliers et les phénomènes malveillants pouvant apparaître au moment de l’usinage. La fonction de diagnostic permet de comprendre les causes de ces défaillances et de proposer des solutions d’amélioration, à travers la réutilisation des connaissances stockées dans l’ontologie du domaine et un raisonnement à base de règles métiers. Le système à base de connaissances proposé est implémenté dans un Framework global d’aide à la décision, développé dans le cadre du projet ANR collaboratif appelé Smart Emma. Une application pratique a été faite sur deux bases de données réelles provenant de deux industriels différents
Article
The latest advances in technology and the improvement of decision processes with learning methods based on artificial intelligence have put the word "smart" ahead of all systems that make human life easier. Based on intelligent transportation systems, it is aimed to reduce the damage to the country's economy and the environment while providing technology-based and faster, safer, more accessible, more sustainable and more efficient transportation. The main goal of creating the intelligent transportation systems architecture is to design and implement human-focused, sustainable transportation systems together with cutting-edge technologies such as industry 4.0 technologies, mobile applications, augmented reality, and the internet of things. The transition to the Cooperative Intelligent Transportation Systems (C-ITS) structure by strengthening the infrastructure of intelligent transportation systems is included in the "National Intelligent Transportation Systems Strategy Document and 2020-2023 Action Plan". Intelligent transportation systems architecture needs to be updated according to C-ITS systems that provide interoperability and data integrity. With C-ITS, the aggregate collection of intelligent systems under one roof and the integrity of data will enable sustainable mobility such as monomedical payment in multi-mode transport. Therefore, the main factor in creating the architecture of intelligent transport systems is to create system architecture by making complex systems with data integrity and numerous insignificant idle data into systems that communicate with each other and reach the level of interoperability. In this study, intelligent transportation systems policies in the world have been analyzed and systems that have reached the level of interoperability that will provide the basis of C-ITS and intelligent transportation systems architecture have been proposed.
Article
With the increased growth of the added population group and advancements in health informatics, the smart aged home needs to be equipped with intelligent systems and various electronic devices. Industry 5.0 is yet another revolution that bought about the transformation in the practices concerning the use of intelligent technology with smart sensors and agent networks. Smart sensors enabled industry 5.0 is equipped with automation and self-monitoring ability that assists with analyzing the issues without the intervention of human beings. We, in this paper, proposed an iterative development process based on the smart sensor and agent networks of the SDLC software development models. The proposed model is developed by synchronizing the smart aged home with the cyber-physical system of industry 5.0. The quality value matrix is introduced as a tool to identify the requirements of the residents and the stakeholders. The feasibility of the proposed work is assessed with the help of an agent-based simulation system.
Article
An efficient maintenance strategy will provide more transparency and competitiveness for manufacturers in smart manufacturing era. Even though there have been several maintenance frameworks considering the emerging technologies, they still need to address challenges like big data storage, system resilience, multi-modality sensing, holistic approach, future-proof, generic framework and so on. To address these challenges, this paper presents a smart maintenance framework for shop-floor. First, a brief insight into key properties, performance indicators, levels and enablers of smart maintenance are described. The conceptual framework is based on distributed intelligence and has two major blocks - innate and adaptive intelligence. Tool wear monitoring is presented as a use case.
Article
Full-text available
Article
Full-text available
Children with autism spectrum disorders (ASDs) often experience difficulties with motor skill learning and performance. The pool is a unique learning environment that can help children with ASDs learn or improve aquatic skills, fitness, and social skills. A pool-based approach is also aligned with the elements of dynamic systems theory, which suggests that movement patterns develop as a result of a complex interaction between the environment, task, and learner. This article introduces pool-based activities for enhancing fundamental motor-skill development in children with ASDs, as well as task, environment, and learner variations. A lesson plan is also provided.
Conference Paper
Full-text available
With rising competition abroad, US manufacturers are looking to reinvest manufacturing capabilities to counterbalance costs by increasing productivity. Being a dynamic and technologically advanced industry, as well as constantly to meet changing market demands, manufacturers are now forced to evolve strategies to manage larger capacity with faster speed, and more sophisticated machinery systems. This paper discusses the principles of predictive manufacturing system as a strategy to allow manufacturing industry to increase competitiveness through a highly transparent and worry-free manufacturing process, as well as an analytic framework how it can be implemented using a coupled-model approach.
Article
Full-text available
The globalization of the world’s economies is a major challenge to local industry and it is pushing the manufacturing sector to its next transformation – predictive manufacturing. In order to become more competitive, manufacturers need to embrace emerging technologies, such as advanced analytics and cyber-physical system-based approaches, to improve their efficiency and productivity. With an aggressive push towards “Internet of Things”, data has become more accessible and ubiquitous, contributing to the big data environment. This phenomenon necessitates the right approach and tools to convert data into useful, actionable information.
Article
Full-text available
Cyber-Physical Systems (CPSs) are characterized by integrating computation and physical processes. The theories and applications of CPSs face the enormous challenges. The aim of this work is to provide a better understanding of this emerging multi-disciplinary methodology. First, the features of CPSs are described, and the research progresses are summarized from different perspectives such as energy control, secure control, transmission and management, control technique, system resource allocation, and model-based software design. Then three classic applications are given to show that the prospects of CPSs are engaging. Finally, the research challenges and some suggestions for future work are in brief outlined.
Conference Paper
Full-text available
This paper presents a similarity-based approach for estimating the Remaining Useful Life (RUL) in prognostics. The approach is especially suitable for situations in which abundant run-to-failure data for an engineered system are available. Data from multiple units of the same system are used to create a library of degradation patterns. When estimating the RUL of a test unit, the data from it will be matched to those patterns in the library and the actual life of those matched units will be used as the basis of estimation. This approach is used to tackle the data challenge problem defined by the 2008 PHM Data Challenge Competition, in which, run-to-failure data of an unspecified engineered system are provided and the RUL of a set of test units will be estimated. Results show that the similarity-based approach is very effective in performing RUL estimation.
Book
Control systems are ubiquitous; a landmark of the control of mechanical systems was James Watt’s governor. Digital computers have been used as controllers since the early days of computing. But control theory and computer engineering have long been regarded as separate fields. Cyber-physical systems (CPS) has recently emerged as a discipline that unites these two disciplines. Just as embedded computing emerged to develop hardware/software co-design, CPS is developing methods for control/computing co-design.
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.