Content uploaded by Attia Hussien Gomaa
Author content
All content in this area was uploaded by Attia Hussien Gomaa on Dec 06, 2024
Content may be subject to copyright.
Engineering Science
2024, Vol. 9, No. 3, pp. 60-70
https://doi.org/10.11648/j.es.20240903.12
*Corresponding author:
Received: 10 October 2024; Accepted: 1 November 2024; Published: 3 December 2024
Copyright: © The Author (s), 2024. Published by Science Publishing Group. This is an Open Access article, distributed
under the terms of the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Research Article
Digital Twins for Improving Proactive Maintenance
Management
Attia Hussien Gomaa*
Mechanical Eng. Department, Faculty of Eng. Shubra, Benha University, Cairo, Egypt
Abstract
Proactive maintenance is a policy aimed at identifying the root cause of failure and correcting it before it causes other problems
and leads to machinery failure and breakdown. Implementing this policy can enhance reliability, availability, maintainability, and
safety (RAMS) at low cost. A digital twin (DT) is a digital copy of a physical object and its applications will play a leading role in
the future of smart manufacturing. DT concept is increasingly appearing in industrial applications including proactive
maintenance, enabling accurate identification of equipment condition, proactive prediction of faults, and enhanced reliability.
This review paper focuses on the performance and applications of different aspects of DTs in proactive maintenance polices. The
review of literature focused on the applications of DT in maintenance management for improving equipment RAMS. The
literature review shows that the application of DT techniques in proactive maintenance remains very important for managing the
maintenance of critical equipment and production systems. Several DT frameworks for proactive maintenance have been
discussed. Furthermore, this study provides a comprehensive roadmap for future research initiatives aiming to fully utilize the
capabilities of technology design teams. Finally, the results of this study will be of value to professionals who want and aspire to
implement technological design to achieve maintenance excellence.
Keywords
Manufacturing, Simulation, Maintenance, Fault Prediction, Digital Twin, Machine Learning, Continuous Improvement
1. Introduction
Proactive maintenance is a proactive policy that aims to
identify, analyze, and correct the root cause of a failure before
it causes further problems and leads to machinery failure.
Implementing this policy can enhance reliability, availability,
maintainability, and safety (RAMS), [11]. As depicted in
Figure 1, a digital twin (DT) is a digital version of a physical
object or system. It can successfully model a virtual object
from its physical counterpart. The main function of a DT is to
provide a two-way data flow between the virtual and physical
entity so that it can continuously upgrade and improve the
physical counterpart, [5, 6]. NASA first used the term digital
twin in 2010, which was described as “an integrated, mul-
ti-physics, multi-scale, probabilistic simulation of a vehicle or
system that uses the best available physics models, sensor
updates, fleet history, etc., to simulate the life of its flying
twin.” [32, 38]. Michael Graves was the first to propose the
term DT, [12, 37]. Recently, DT has been utilized in various
manufacturing fields, and it is promoting positive develop-
ments in these fields, [16, 19]. Kritzinger, [23] recognized
three levels of DT integration, namely digital model, digital
Engineering Science http://www.sciencepg.com/journal/es
61
shadow, and digital twin, as shown in Figure 2. Attaran, [3]
mentioned the main DT applications in manufacturing, as
presented in Figure 3. Many diagnostic tools are available to
identify and analyze the root causes of failures. Failure Mode
Effects and Criticality Analysis (FMECA) is the most com-
mon diagnostic method, which consists of two analyses; the
Failure Mode and Effects Analysis (FMEA) and the Critical-
ity Analysis (CA), [18, 40, 11, 37, 43]. DT enables mainte-
nance management to accurately identify equipment status,
proactively predict faults, and enhance reliability, [1, 2, 4, 26,
47]. DT contains a set of adaptive models that can emulate the
behavior of a physical system in a virtual system, obtaining
real-time data to update itself along its life cycle, [39, 3].
Figure 4 shows an equivalent representation of the general
architecture of DT, [55].
Figure 1. Digital twin illustration
Figure 2. Digital twin Levels of integration.
Figure 3. Digital twin applications in manufacturing.
Engineering Science http://www.sciencepg.com/journal/es
62
Figure 4. Equivalent representation of the general architecture of DT.
This study focuses on the performance and applications of
DTs in proactive maintenance policies and the importance of
DT in maintenance management for improving equipment
RAMS (reliability, availability, maintainability, and safety).
This study is an extension of a previously published paper,
[10].
After this introduction, this paper is organized as follows:
In Section 2, the literature review is carried out. In Section 3,
the research gap is identified. Section 4 includes the DT
framework for proactive maintenance. Finally, Section 5
focuses on conclusions and future directions.
2. Literature Review
Digital twins (DT) can provide a real-time response to the
manufacturing system and increase flexibility and reliability,
[13]. According to Hu, [16] Figure 5 illustrates some of the
key milestones in the development of DT. In 2016, Siemens
used DT devices in Industry 4.0, resulting in a tremendous
growth in related publications.
Figure 5. The milestones of DT development.
Engineering Science http://www.sciencepg.com/journal/es
63
Proactive maintenance can reduce failure risks, improve
system uptime, extend the equipment life, and lower process
down time losses. DT can model individual equipment or
processes to identify variations that indicate the need for
preventive maintenance. The goal is to estimate, predict,
detect, or diagnose the condition of the component for more
effective maintenance. This can prevent costly failures before
a serious problem occurs. They can also determine if better
materials or processes can be used or help improve cycle
times, load levels, and tool calibrations, [20, 46]. The appli-
cation of DTs enables the monitoring of the condition and
prediction of abnormal conditions in machine tools. This
greatly enhances the safe and efficient operation of mechan-
ical process systems. Parameter optimization plays a crucial
role in the optimization of the operation process. Traditional
parameter optimization methods rely on manual experience
and often involve high levels of uncertainty. DT operation
process facilitates the suppression of errors and the optimiza-
tion of operating parameters, thus laying the foundation for
achieving high-quality and high-level operation, [16].
DT represents the innovation that has spurred evolution and
adaptation in the aerospace industry. For instance, employing
DT for an aircraft or rocket ship is believed to enhance global
tracking accuracy by 147%. In a recent survey, 75% of Air
Force executives favored DT solutions for their industry. DT
enables engineers to ensure the safety of the aircraft by
looking into the potential aircraft‟s problem before any danger.
For example, Boeing, the world‟s largest aerospace company,
uses DT solutions to improve the safety of the parts and sys-
tems used to manufacture commercial and military airplanes.
DTs of specific aircraft models enable technicians to use
augmented reality (AR) overlaying the DT data on the real
plane, facilitating faster and more accurate inspections and
improving maintenance efficiency. As a result, Boeing has
achieved a 40 percent improvement in the quality of the parts
and systems, [29, 49]. According to GE Research, [9] GE‟s
DT technology is revolutionizing how the aviation industry
handles maintenance. Predicting engine wear, such as the
blade wear on the GE90, saves airlines millions of dollars in
costs and prevents aircraft from landing unexpectedly, espe-
cially in areas with sand, a major contributor to the problem.
According to Pinello [33], the European Space Agency
(ESA) also adopted the DT approach for its ExoMars mission.
They built Amalia, a physical and DT of the Rosalind Franklin
rover. This duo serves a vital purpose: anticipating and solv-
ing potential problems before they occur on the Martian sur-
face. Overall, using both physical and DTs significantly in-
creases mission success by minimizing risks and improving
rover performance.
DTs have been increasingly used in condition monitoring
and fault diagnosis (CMFD) in recent years. Table 1 shows the
survey of DTs in maintenance over the past years. The details
of these studies are explained in the next section.
Table 1. Survey of DTs in Proactive Maintenance, (2017 to August
2024).
Period
References
Before 2017
-
2017
[42]
2018
[44]
2019
[2, 41, 34, 52]
2020
[7, 14, 25]
2021
[27, 31, 35, 48, 50, 51, 58]
2022
[10, 15, 22, 30, 45, 55, 56]
2023
[25, 28, 36, 46, 48, 54, 57]
August 2024
[8, 17, 21, 24, 53]
Tao, [42] adopted the concept of a DTs workshop, provid-
ing theoretical support for industry applications by discussing
its characteristics, composition, operating mechanism, and
key technologies. Tao, [44] suggested a five-dimension DT
model for complex systems to improve the accuracy of
prognosis.
Qiao, [34] developed a data-driven model for DT, together
with a hybrid model prediction method based on deep learn-
ing that creates a prediction technique for enhanced machin-
ing tool condition prediction. Xu, [52] studied a two-stage
DT-assisted method based on deep migration learning. This
method identifies potential problems that may not have been
considered during the design phase and uses deep neural
network-based diagnostic models for fault diagnosis. Ai-
valiotis, [2] presented a methodology to calculate the Re-
maining Useful Life (RUL) of machinery equipment by uti-
lizing physics-based simulation models and the DT concept,
to enable predictive maintenance for manufacturing resources
using Prognostics and health management (PHM) techniques.
Luo, [25] suggested a hybrid DT model that consists of
model-based DTs and data-driven DTs to take into consider-
ation the environmental variations in the life cycle of the tool.
To realize reliable predictive maintenance of CNC machine
tools, a hybrid approach driven by DT is studied. Xia, [50]
developed a DT model for machinery fault diagnosis where
the DT is built by establishing the simulation model which can
be updated through the real-time data collected from the
physical asset. The proposed DT is validated through a case
study of triplex pump fault diagnosis. Xiong, [51] investigated
the predictive maintenance model of an aero-engine driven by
DTs. Through the consistent evaluation of virtual data assets
and real data assets, the effectiveness of the model is verified.
Experimental results show that when the dataset used to train
the model is 80%, the model prediction has high accuracy.
Engineering Science http://www.sciencepg.com/journal/es
64
Wang, [48] developed a DT model including a geometric
model, physical model, behavior model, and rule model to
perform fault prediction of the autoclave to generate simu-
lated data to address the problem of insufficient data for fault
prediction. The effectiveness of the proposed model is veri-
fied through result analysis. Olatunji, [31] discussed an
overview of the application of DT technology in the fault
diagnosis and condition monitoring of wind turbine mechan-
ical components. Qin, [35] proposed a DT model of life-cycle
rolling bearing driven by the data-model combination. By
comparing the obtained DT result with the signal measured in
the time domain and frequency domain, the effectiveness of
the developed model is verified.
Refer to Xiong, [51] DTs solutions are widely used in the
aerospace industry for aircraft maintenance and tracking,
weight monitoring, accurate determination of weather condi-
tions, flight time measurement, catastrophic failure analysis,
safety and security management, and failure detection.
Moghadam and Nejad, [30] presented a DT-based condition
monitoring and fault diagnosis (CMFD) approach for offshore
drivetrain systems, where the DT in the study includes a tor-
sional dynamic model, online measurements, and fatigue
damage estimation. The remaining useful life of the drivetrain
can be estimated by means of the DT. Kim, [22] utilized
various environmental information to design a predictive
model for offshore WT power generation based on DT. The
proposed system enables an accurate representation of the
offshore WT power generation and makes contributions to the
safety of the power system. Hosamo, [15] suggested a DT
predictive maintenance framework for air handling units
(AHU) to overcome the limitations of facility maintenance
management (FMM) systems now in use in buildings. The
proposed framework was tested in a real-world case study.
Zhong, [57] reviewed the increasing research interest in
DTs-based predictive maintenance in the manufacturing in-
dustry. The predictive maintenance approaches based on DTs
are introduced. Wang, [48] proposed a real-time planetary
gear fault diagnosis method by combining the atom search
optimization-support vector machine and DTs which can
significantly improve the operation of wind turbines.
Reimann, [36] developed a DT model of a wind turbine. The
model was evaluated in simulations using real measurement
data of the wind speed from a research wind turbine. Luo, [25]
suggested a DT system for wind turbine blades, which can
construct a DT in virtual space that is completely equivalent to
the wind turbine blades, reflecting in real time the operational
data and status of the wind turbine blades, and realizing online
monitoring and predictive maintenance of the wind turbine
blades. Van-Dinter, [46] conducted domain analysis to model
key features and synthesize relevant literature. A case study
on fault diagnosis using DFDD in a vehicle body-side pro-
duction line is presented. The results demonstrate the superi-
ority and applicability of the proposed method. Yang, [54]
developed a complex fault diagnosis method using DT by
combining virtual and real data. Field data from an offshore
platform in the South China Sea were used to demonstrate the
effect of the suggested method. The results indicate that the
proposed method is very effective for complex faults of pro-
duction control systems.
Inturi, [17] reported a review study focusing on the defini-
tions, methods, applications, and performance of different
aspects of DTs in the context of transportation and industrial
machinery. This review summarizes how individual aspects of
DTs are extremely useful for lifelong design, manufacturing,
or decision-making. Liu, [24] developed an innovative
DT-based anomaly detection framework for real-time tool
condition monitoring (TCM). The „„data flow connections‟‟
involve real-time measured vibration data and machine tool
numerical controller (NC) signals providing real-time infor-
mation on machine tool dynamics and various machining
processes. Experimental studies have demonstrated the ef-
fectiveness of the proposed method, especially for compli-
cated machining processes. Gao, [8] discussed the concept of
post-disaster recovery for power DTs systems to study ra-
tional approaches to enhance the post-disaster monitoring
capability of such systems after significant disasters. The
results indicate that the proposed branch-and-limit algorithm
greatly enhances the monitoring capabilities of the re-
source-constrained power system, thus enhancing its stability
and emergency response mechanisms. Xue, [53] developed a
DT-driven fault diagnosis method for CNC machine tools. By
using the spindle of a CNC machine as an example, the dete-
rioration of spindle stiffness during operation is effectively
diagnosed, which confirms the effectiveness and applicability
of the proposed method. Karkaria, [21] discussed a DT
framework for predictive maintenance of long-term physical
systems. Using tire health monitoring as an application, they
demonstrate how the DT framework can be used to enhance
the safety and efficiency of automobiles. The proposed
framework effectively embodies a physical system, leverag-
ing big data and machine learning for predictive maintenance,
model updates, and decision-making.
3. Research Gap Analysis
The literature review shows that the application of DT
techniques in proactive maintenance remains very important
for managing the maintenance of critical equipment to im-
prove equipment RAMS (reliability, availability, maintaina-
bility, and safety) and achieve maintenance excellence.
However, there is still a need for a common platform based on
creating a physical model via a common methodology. This is
a requirement for implementing the DT concept of proactive
maintenance. Moreover, implementing DT technology, for
maintenance activities in a production plant, requires creating
a DT for each machine. Finally, a more detailed review of the
literature should also be conducted to identify further gaps,
which will be addressed within the framework of constructing
and fine-tuning the proposed model.
Engineering Science http://www.sciencepg.com/journal/es
65
4. Digital Twin Frameworks for
Proactive Maintenance
As mentioned earlier, manufacturing maintenance costs and
downtime losses are very high in different sectors, which justi-
fies the investment in creating DTs to optimize maintenance
activities. Figure 6 shows a DT model in maintenance, [10].
According to Dihan [5], data analysis is the technology
driver of a successful DT system. Since data is the funda-
mental difference between a successful and unsuccessful
system, proper guidance of data structure should be given due
attention. Figure 7 shows the DT data analysis process for
building a successful DT.
Figure 6. DT model in maintenance.
Figure 7. DTs Data analysis process, [5].
Hosamo, [15] suggested a DT predictive maintenance
framework for air handling units (AHU). The proposed
framework utilizes DT technology for fault detection and
diagnostics and predicts the condition of the building com-
ponents so that the facility management staff can make better
decisions at the right time. Figure 8 shows the principle of a
DT in proactive maintenance. The proposed framework in-
cludes three main steps, Data acquisition, predictive mainte-
nance process, and BIM model for information visualization
and monitoring. Spatial information can be obtained from the
BIM model. The BIM model was integrated with predictive
maintenance results to support decision-making by develop-
ing a plug-in extension for Autodesk Revit using C sharp so
that the FM team can easily understand the data. The three
main levels of this framework will be explained in detail in the
following sections. For facility management, COBie (Con-
struction Operations Building Information Exchange) and
Industrial Foundation Classes (IFC) are information exchange
specifications for the lifetime capture and transfer of infor-
mation. Figure 9 shows COBie components.
Engineering Science http://www.sciencepg.com/journal/es
66
Figure 8. DT predictive maintenance framework, [15].
Figure 9. Standard COBie components, [15].
Mihai et al., [27] developed a framework that aims to
achieve optimized predictive maintenance by leveraging
predominantly time-indexed streaming sensor data, along
with configuration data coming from the digital twin of the
Cyber-Physical Factory. The developed framework is illus-
trated in Figure 10, which consists of: the data acquisition
block, the pre-processing block, the database, the time-series
anomaly detection block, the RUL predictor block, and the
monitoring dashboard.
Engineering Science http://www.sciencepg.com/journal/es
67
Figure 10. A Digital Twin Framework for Predictive Maintenance. [27]
Karkaria, [21] introduced a DT framework for proactive
maintenance of long-term physical systems. Figure 11 shows
the tire health DT framework demonstrating the flow of in-
formation, and important components of DT like offline
training, model update, and decision making. As shown in this
figure, the digital twin begins with the offline training of the
Tire Health Temporal Fusion Transformer (TFT) model in
Step 1, leveraging historical datasets which has operating
parameters (𝑇!) - conditions under which the tire operates,
usage parameters (𝑈!) - how the tire is used, and state pa-
rameters (𝑀!) - the current condition of the tire. Additionally,
within our digital twin framework, we get our dataset with
inputs derived from a physics-based Tire Design Finite Ele-
ment Method (FEM) integrating physical insights with
measured data. Incorporating a physics-based Tire Design
Finite Element Method (FEM) is crucial to accurately under-
stand the tire's physics-based state, ensuring a comprehensive
analysis of its condition through the integration of physical
principles with observed data. Then the Tire Health TFT
model, a critical component of the Tire Health Digital Twin,
facilitates real-time predictions of the damage state. A con-
tinuous quantity, named as Remaining Casing Potential (RCP),
is considered as the damage state parameter). RCP serves as a
key indicator of tire endurance damage, allowing for proac-
tive maintenance decisions. The predictions by the Tire
Health TFT model are subsequently compared with re-
al-world instances of tire damages. This comparison allows us
to quantify the discrepancy, effectively measuring the differ-
ence between the model's predictions and the actual tire
damage data in Step 2. We utilize observed discrepancies to
refine our Tire Health TFT model. It is important to highlight
that, following an update, our model evolves into a hybrid
version. Despite this transformation, we continue to refer to it
as the Tire Health TFT model for consistency and clarity in
our discussion in this paper. Then the updated Tire Health
TFT model, with the Tire State Decision Algorithm in Step 3,
informs timely tire replacement decisions. Thus, our tire
health digital twin has the surrogate model, which is updated
in real-time, and aids in making predictive maintenance de-
cisions.
Figure 11. Tire health digital twin framework, [21].
Engineering Science http://www.sciencepg.com/journal/es
68
5. Conclusion and Further Work
Proactive maintenance is a policy that aims to identify the
root cause of a failure and correct it before it causes further
problems and leads to machinery failure. Implementing this
policy can enhance reliability, availability, maintainability,
and safety (RAMS). This paper focuses on reviewing the
applications of digital twins (DT) in proactive maintenance.
DT can be used as a data-driven digital concept or technology
to effectively address critical equipment maintenance issues.
DT enables maintenance management to accurately determine
equipment status, proactively predict faults, and enhance
reliability. The application of DT technologies remains a
critical proactive technology for critical equipment to improve
equipment RAMS and achieve maintenance excellence.
Several DT frameworks for proactive maintenance have been
discussed. Furthermore, this study provides a comprehensive
roadmap for future research initiatives aiming to fully utilize
the capabilities of technology design teams.
In future activities, the author plans to integrate DT meth-
odology and Lean Six Sigma approach into a more general
maintenance management framework for critical equipment
whose main role will be to assess and improve the health
status of machines, improve reliability, and plan maintenance
activities.
Abbreviations
DT
Digital Twins
RAMS
Reliability, Availability, Maintainability, and
Safety
Author Contributions
Attia Hussien Gomaa is the sole author. The author read
and approved the final manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Aivaliotis, P., Georgoulias, K. and Alexopoulos, K., (2019 a),
“Using digital twin for maintenance applications in
manufacturing: State of the Art and Gap analysis. In 2019
IEEE International Conference on Engineering, Technology
and Innovation (ICE/ITMC) (pp. 1-5). IEEE.
https://www.researchgate.net/publication/333893836
[2] Aivaliotis, P., Georgoulias, K., Chryssolouris, G., (2019 b),
“The use of Digital Twin for predictive maintenance in
manufacturing”, International Journal of Computer Integrated
Manufacturing, 32: 11, pp. 1067-1080.
https://doi.org/10.1080/0951192X.2019.1686173
[3] Attaran, S., Attaran, M. and Celik, B. G., (2024), “Digital
Twins and Industrial Internet of Things: Uncovering
operational intelligence in industry 4.0”, Decision Analytics
Journal, 10, p. 100398.
https://doi.org/10.1016/j.dajour.2024.100398
[4] Chen, C., Fu, H., Zheng, Y., Tao, F., Liu, Y., (2023), “The
advance of digital twin for predictive maintenance: the role
and function of machine learning”, J Manuf Syst, vol. 71, pp.
581-594. https://doi.org/10.1016/j.jmsy.2023.10.010
[5] Dihan, M. S., Akash, A. I., Tasneem, Z., Das, P., Das, S. K.,
Islam, M. R., Islam, M. M., Badal, F. R., Ali, M. F., Ahmed,
M. H. and Abhi, S. H., 2024. Digital twin: Data exploration,
architecture, implementation and future. Heliyon.
[6] Emmert-Streib, F., 2023. Defining a digital twin: A data
science-based unification. Machine Learning and Knowledge
Extraction, 5(3), pp. 1036-1054.
https://doi.org/10.3390/make5030054
[7] Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital
Twin for maintenance: A literature review. Computers in
Industry, 123, 103316.
https://doi.org/10.1016/j.compind.2020.103316
[8] Gao, S., Wang, W., Chen, J., Wu, X. and Shao, J., 2024.
Optimal decision-making method for equipment maintenance
to enhance the resilience of power digital twin system under
extreme disaster. Global Energy Interconnection, 7(3), pp.
336-346. https://doi.org/10.1016/j.gloei.2024.06.005
[9] GE Research, Digital twin creation, 2023, Retrieved
12-27-2023.
https://www.ge.com/research/offering/digital-twin-creation
[10] Gomaa, Attia H., (2022), “Enhancing Maintenance
Management of Critical Equipment Using Digital Twin”,
Comprehensive Research and Reviews in Engineering and
Technology, (CRRET), vol. 1, no. 1, pp. 45-55.
https://doi.org/10.58175/gjret.2022.1.1.0015
[11] Gomaa, Attia H., (2024), “Improving Shutdown Maintenance
Management Performance Using Lean Six Sigma Approach:
A Case Study”, International Journal of Applied and Physical
Sciences, IJAPS, Vol. 10, No. 1, pp. 1-14.
https://dx.doi.org/10.20469/ijaps.10.50001
[12] Grieves, M., (2014), "Digital twin: Manufacturing excellence
through virtual factory replication", White Paper, vol. 1, no.
2014, pp. 1-7.
[13] He, B. and Bai, K. J., (2021) "Digital twin-based sustainable
intelligent manufacturing: A review," Adv. Manuf., vol. 9, no.
1, pp. 1-21. https://doi.org/10.1007/s40436-020-00302-5
[14] Heim, S., Clemens, J., Steck, J. E., Basic, C., Timmons, D.
and Zwiener, K., (2020), December. Predictive maintenance
on aircraft and applications with digital twin. In 2020 IEEE
International Conference on Big Data (Big Data), pp.
4122-4127.
https://doi.org/10.1109/BigData50022.2020.9378433
Engineering Science http://www.sciencepg.com/journal/es
69
[15] Hosamo, H. H., Svennevig, P. R., Svidt, K., Han, D. and
Nielsen, H. K., 2022. A Digital Twin predictive maintenance
framework of air handling units based on automatic fault
detection and diagnostics. Energy and Buildings, 261, p.
111988. https://doi.org/10.1016/j.enbuild.2022.111988
[16] Hu, S., Li, C., Li, B., Yang, M., Wang, X., Gao, T., Xu, W.,
Dambatta, Y. S., Zhou, Z. and Xu, P., (2024). Digital Twins
Enabling Intelligent Manufacturing: From Methodology to
Application. Intelligent and Sustainable Manufacturing, 1(1),
p. 10007, pp. 1-22. https://doi.org/10.35534/ism.2024.10007
[17] Inturi, V., Ghosh, B., Rajasekharan, S. G. and Pakrashi, V.,
(2024). A Review of Digital Twinning for Rotating Machinery.
Sensors, 24(15), p. 5002, pp. 1-33.
https://doi.org/10.3390/s24155002
[18] Jay, L., W. Fangji, Z. Wenyu, G. Masoud, L. Linxia, and S.
David., (2014), “Prognostics and Health Management Design
for Rotary Machinery systems -Reviews Methodology and
Applications”, Mechanical Systems and Signal Processing 42,
pp. 314–334. https://doi.org/10.1016/j.ymssp.2013.06.004
[19] Jones, D., Snider, C., Nassehi, A., Yon J. and Hicks, B., (2020),
"Characterising the digital twin: A systematic literature
review", CIRP J. Manuf. Sci. Technol., vol. 29, pp. 36-52.
https://doi.org/10.1016/j.cirpj.2020.02.002
[20] Kang, Z., Catal, C. and Tekinerdogan, B., (2021), “Remaining
useful life (RUL) prediction of equipment in production lines
using artificial neural networks” Sensors, 21(3), p. 932.
https://doi.org/10.3390/s21030932
[21] Karkaria, V., Chen, J., Luey, C., Siuta, C., Lim, D., Radulescu,
R. and Chen, W., 2024. A Digital Twin Framework Utilizing
Machine Learning for Robust Predictive Maintenance:
Enhancing Tire Health Monitoring. arXiv preprint arXiv:
2408. 06220.https://doi.org/10.48550/arXiv.2408.06220
[22] Kim, C., (2022), "Design, implementation, and evaluation of
an output prediction model of the 10 mw floating offshore
wind turbine for a digital twin," Energies, vol. 15, no. 17, art
no. 6329, pp. 1-16. https://doi.org/10.3390/en15176329
[23] Kritzinger, W., Karner, M., Traar, G., Henjes, J. and Sihn, W.,
2018. Digital Twin in manufacturing: A categorical literature
review and classification. Ifac-PapersOnline, 51(11), pp.
1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474
[24] Liu, Z.; Lang, Z. Q.; Gui, Y.; Zhu, Y. P.; Laalej, H., (2024),
“Digital twin-based anomaly detection for real-time tool
condition monitoring in machining”, J. Manuf. Syst., 75, pp.
163–173. https://doi.org/10.1016/j.jmsy.2024.06.004
[25] Luo W, Hu T, Ye Y, Zhang C, Wei Y., (2020), “A hybrid
predictive maintenance approach for CNC machine tool
driven by digital twin”, Robot Comput-Integr Manuf, 65:
101974, pp. 1-16. https://doi.org/10.1016/j.rcim.2020.10197
[26] Luo, H., Wang, L., Sun, W. and Lu, C., (2023), “Intelligent
Monitoring and Maintenance of Wind Turbine Blades Driven
by Digital Twin Technology”, 3rd International Conference
on New Energy and Power Engineering (ICNEPE), pp.
626-630.
https://doi.org/10.1109/ICNEPE60694.2023.10429668
[27] Mihai, S., Davis, W., Hung, D., Trestian, R., Karamanoglu, M.,
Barn, B., Prasad, R., Venkataraman, H. and Nguyen, H. 2021.
A digital twin framework for predictive maintenance in
industry 4.0. HPCS 2020: 18th Annual Meeting. Barcelona,
Spain (Online Virtual Conference) 22 - 27 Mar 2021 IEEE.
[28] Minghui, H. U., Ya, H. E., Xinzhi, L. I. N., Ziyuan, L. U., Jiang,
Z. and Bo, M. A., 2023. Digital twin model of gas turbine and
its application in warning of performance fault. Chinese
Journal of Aeronautics, 36(3), pp. 449-470.
https://doi.org/10.1016/j.cja.2022.07.021
[29] Miskinis, C., 2019. Future role of digital twin in the aerospace
industry. January.
https://www.challenge.org/insights/digital-twinin-aerospace
[30] Moghadam FK, Nejad AR., (2022), “Online condition
monitoring of floating wind turbines drivetrain by means of
digital twin”, Mech Syst Signal Process, 162: 108087, pp.
1-26. https://doi.org/10.1016/j.ymssp.2021.108087
[31] Olatunji, O. O., Adedeji, P. A., Madushele, N., & Jen, T.-C.
(2021), “Overview of Digital Twin Technology in Wind
Turbine Fault Diagnosis and Condition Monitoring”, IEEE
12th International Conference on Mechanical and Intelligent
Manufacturing Technologies (ICMIMT), pp. 1-7.
https://doi.org/10.1109/ICMIMT52186.2021.9476186
[32] Onaji, I., Tiwari, D., Soulatiantork, P., Song, B. and Tiwari, A.,
(2022), “Digital twin in manufacturing: conceptual framework
and case studies”, International journal of computer integrated
manufacturing, 35(8), pp. 831-858.
https://doi.org/10.1080/0951192X.2022.2027014
[33] Pinello, L., Giglio, M., Cadini, C. and De Luca, G. F., 2023,
September. Development of a space exploration rover digital
twin for damage detection. In PHM Society Asia-Pacific
Conference (Vol. 4, No. 1).
https://doi.org/10.36001/phmap.2023.v4i1.3628
[34] Qiao, Q., Wang, J., Ye, L. and Gao, R. X., (2019), “Digital twin
for machining tool condition prediction”, Procedia CIRP, 81, pp.
1388-1393. https://doi.org/10.1016/j.procir.2019.04.049
[35] Qin, Y., Wu, X. and Luo, J., (2021). Data-model combined
driven digital twin of life-cycle rolling bearing. IEEE
Transactions on Industrial Informatics, 18(3), pp. 1530-1540.
https://doi.org/10.1109/TII.2021.3089340
[36] Reimann, R., Menzel, S., Holzke, W., Raffel, H. and Orlik, B.,
(2023), “Development and Evaluation of a Model for the
Implementation of a Digital Twin for a Wind Turbine”, In PCIM
Europe 2023; International Exhibition and Conference for Power
Electronics, Intelligent Motion, Renewable Energy and Energy
Management, pp. 1-9. https://doi.org/10.30420/566091280
[37] Semeraro, C., Lezoche, M., Panetto, H. and Dassisti, M., 2021.
Digital twin paradigm: A systematic literature review.
Computers in Industry, 130, p. 103469.
https://doi.org/10.1016/j.compind.2021.103469
[38] Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C.,
LeMoigne, J. and Wang, L., (2010), “Draft modeling,
simulation, information technology & processing roadmap”,
Technology area, 11, pp. 1-32.
Engineering Science http://www.sciencepg.com/journal/es
70
[39] Soori, M., Arezoo, B. and Dastres, R., 2023. Digital twin for
smart manufacturing, A review. Sustainable Manufacturing
and Service Economics, p. 100017.
https://doi.org/10.1016/j.smse.2023.100017
[40] Spreafico, C., D. Russo, and Rizzi, C., (2017), “A
State-Of-the-art Review of FMEA / FMECA Including
Patents”, Computer Science Review 25, pp. 19–28.
https://doi.org/10.1016/j.cosrev.2017.05.002
[41] Tao F, Liu W, Zhang M, Hu T-L, Qi Q, Zhang H, (2019),
“Five-dimension digital twin model and its ten applications”,
Comput. Integr. Manuf. Syst., 25, pp. 1-18.
[42] Tao F, Zhang M, Cheng J, Qi Q., (2017), “Digital twin
workshop: a new paradigm for future workshop”, Comput.
Integr. Manuf. Syst. 23, pp. 1-9.
[43] Tao F, Zhang M, Liu Y, Nee AY, (2019), "Digital Twin in
Industry: State-of-the-Art," in IEEE Transactions on Industrial
Informatics, vol. 15, no. 4, pp. 2405-2415.
https://doi.org/10.1109/TII.2018.2873186
[44] Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital
twin driven prognostics and health management for complex
equipment. CIRP Annals, 67(1), pp. 169-172.
https://doi.org/10.1016/j.cirp.2018.04.055
[45] Van Dinter, R., Tekinerdogan, B. and Catal, C., (2022),
“Predictive maintenance using digital twins: A systematic
literature review”, Information and Software Technology, 151,
p. 107008. https://doi.org/10.1016/j.infsof.2022.107008
[46] Van Dinter, R., Tekinerdogan, B. and Catal, C., (2023),
“Reference architecture for digital twin-based predictive
maintenance systems”, Computers & Industrial Engineering,
177, p. 109099, pp. 1-24.
https://doi.org/10.1016/j.cie.2023.109099
[47] Wang, Y., Tao, F., Zhang, M., Wang, L., & Zuo, Y. (2021).
Digital twin enhanced fault prediction for the autoclave with
insufficient data. Journal of Manufacturing Systems, 60, pp.
350–359. https://doi.org/10.1016/j.jmsy.2021.05.015
[48] Wang, Y.; Sun, W.; Liu, L.; Wang, B.; Bao, S.; Jiang, R.,
(2023), “Fault Diagnosis of Wind Turbine Planetary Gear
Based on a Digital Twin”, Applied Sciences, vol. 13, no. 8, art
no. 4776, pp. 1-19. https://doi.org/10.3390/app13084776
[49] Woodrow III, B., (2018), “Boeing CEIO talks „digital
twin‟era of aviation”.
[50] Xia, M., Shao, H., Williams, D., Lu, S., Shu, L., & de Silva, C.
W., (2021), “Intelligent fault diagnosis of machinery using
digital twin-assisted deep transfer learning”, Reliability
Engineering & System Safety, 215, 107938, pp. 1-9.
https://doi.org/10.1016/j.ress.2021.107938
[51] Xiong, M., Wang, H., Fu, Q. and Xu, Y., (2021), “Digital
twin–driven aero-engine intelligent predictive maintenance”,
The International Journal of Advanced Manufacturing
Technology, 114(11), pp. 3751-3761.
https://doi.org/10.1007/s00170-021-06976-w
[52] Xu, Y., Sun, Y., Liu, X. and Zheng, Y., (2019), “A
digital-twin-assisted fault diagnosis using deep transfer
learning”, Ieee Access, 7, pp. 19990-19999.
https://doi.org/10.1109/ACCESS.2018.2890566
[53] Xue, R., Zhang, P., Huang, Z. and Wang, J., 2024. Digital
twin-driven fault diagnosis for CNC machine tool. The
International Journal of Advanced Manufacturing Technology,
131(11), pp. 5457-5470.
https://doi.org/10.1007/s00170-022-09978-4
[54] Yang, C., Cai, B., Wu, Q., Wang, C., Ge, W., Hu, Z., Zhu, W.,
Zhang, L. and Wang, L., 2023. Digital twin-driven fault
diagnosis method for composite faults by combining virtual
and real data. Journal of Industrial Information Integration, 33,
p. 100469. https://doi.org/10.1016/j.jii.2023.100469
[55] You, Y., Chen, C., Hu, F., Liu, Y. and Ji, Z., 2022. Advances
of digital twins for predictive maintenance. Procedia
computer science, 200, pp. 1471-1480.
https://doi.org/10.1016/j.procs.2022.01.348
[56] Zhao, J., Feng, H., Chen, Q. and de Soto, B. G., (2022),
“Developing a conceptual framework for the application of
digital twin technologies to revamp building operation and
maintenance processes”, Journal of Building Engineering, 49,
p. 104028. https://doi.org/10.1016/j.jobe.2022.104028
[57] Zhong, D., Xia, Z., Zhu, Y. and Duan, J., (2023), “Overview
of predictive maintenance based on digital twin technology”,
Heliyon, 9(4), pp. 1-23.
https://doi.org/10.1016/j.heliyon.2023.e14534
[58] Zhou, C., Xiao, D., Hu, J., Yang, Y., Li, B., Hu, S., Demartino,
C. and Butala, M., 2022. An example of digital twins for
bridge monitoring and maintenance: preliminary results. In
Proceedings of the 1st Conference of the European
Association on Quality Control of Bridges and Structures:
EUROSTRUCT 2021 1(pp. 1134-1143). Springer
International Publishing.
https://doi.org/10.1007/978-3-030-91877-4_129