The DOI matches its Crossref record; 59 of the 62 references it cites were found in a registry or an index.
Digital Twin-driven online anomaly detection for an automation system based on edge intelligence
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“Prior studies have predominantly applied Digital Twins to simulation, prediction, or as virtual substitutes for individual sensors [4,13]; by contrast, this work explores their use for system-level redundancy and automated failover at the edge.”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [4]
In the citing paper
“While model-based DTs have been extensively studied for forecasting, anomaly detection, and what-if analysis [3,4,14], the notion of a twin that exists not to model the system but to become the system upon failure remains unexplored.”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [4]
In the citing paper
“These twins observe and model the system but do not themselves become the system.”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [4]
In the citing paper
“Recent work has emphasized predictive failure mitigation using Digital Twins for forecasting and planning [3,4,14,17,18].”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [4]
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“In addition to flexibility and scalability, SDN and NFV can also enhance the security architecture of 6G by providing comprehensive visibility and control over network traffic [5].”
BackgroundNguyen, L. H., Tran, K. D., Zeng, X., & Tran, K. P. (2024) · Springer series in reliability engineering · doi:10.1007/978-3-031-71495-5_5 · Cited by 4
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BackgroundAdvancing Digital Twin Implementation using Edge Adapters based on Containerization
Huang, H., & Xu, X. W. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260410 · Cited by 4
“presented an edge-based Digital Twin for online anomaly detection for automation systems [11].”
MethodSelf-citationDigital Twins for Marine Operations: From Surface to Deep Water
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“Huang et al. (2021) have performed an early industrial system failure detection and proactive maintenance plan management.”
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“…for complex systems in the electrical engineering such as the power grids [1], [2], the power electronics [3], the electric vehicles [4], [5], and the industrial automation [6], [7], realizing diverse functions, including the predictive maintenance [8], [9], and failure diagnosis [9], [10].”
Human centric general physical intelligence for agile manufacturing automation
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“Integrating edge-AI trust engines and consensus mechanisms further enhances resilience by enabling real-time anomaly detection, predictive maintenance and trust scoring among heterogeneous agents even in adversarial or uncertain environments [189][190].”
Towards a UHF RFID Electromagnetic Fingerprint-Based Web Resolver for Digital Twins
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VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
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“The technique presented in [9], with minor modifications to boost the anomaly detection capabilities: the number of decision trees is increased from 5 to 30 and the dimensionality reduction step is removed.”
Sustainable business decision modelling with blockchain and digital twins: a survey
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“While Eqn.4 is an ideal generalization of condition synthesis, it is rare to find either the high-fidelity analytical expression g c ϵ,i ( · ) , or its required inputs readily available [23] in practice.”
BackgroundA Digital Twin Based Framework for Real-Time Machine Condition Monitoring
Chen, Z., Choudhury, M. D., Blincoe, K., & Dhupia, J. S. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260377 · Cited by 2
“In such scenarios, edge computing technology can provide a solution [5].”
BackgroundDigital Twin Modeling Framework for Manual Warehouses
Elbouzidi, A. D., Pellerin, R., Cadi, A. A. E., Lamouri, S., & Boubaker, S. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10393966 · Cited by 2
“This approach allows for the model to be automatically retrained as new data is made available [41], and in the perspective of manual processes, this helps create an evolving simulation environment that is aware of human actions.”
MethodDefining a System Architecture for Operational Digital Twins for Predictive Maintenance
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Solar-Powered Automated Drone for Industrial Safety and Anomaly Detection
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BackgroundDesign and machining analysis of a novel wheel-based mobile machine tool
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InfluentialRoSA:A Mechatronically Synthesized Dataset for Rotodynamic System Anomaly Detection
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“For example, a digital twin-based agent is developed in [3] using 15-days’ of historical data of an industrial chiller.”
BackgroundMethodInfluentialA Review of the Concept, Applications, Risks and Control Strategies for Digital Twin
Farid, F., Bello, A., Jahan, N., & Sultana, R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-54820-8_21 · Cited by 3
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“…new data-driven monitoring and management mechanisms, leveraging the interconnection between the physical and virtual domain created by such a technology to improve, for instance, the energy efficiency of production plants [9] or anomaly detection [10] and predictive maintenance processes [11].”
85Chapter 5 Patient education through metaverse and digital twins
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“Edge computing enhances Digital Twins by enabling local AI to process high-frequency sensor data on-site, reducing latency [15].”
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TWINE: ISO 23247-compliant digital twin and explainable AI framework for anomaly detection
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Research on engine cooling system condition monitoring based on deep digital twin
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“Huang et al. 12 constructed a baseline model for a refrigeration machine using gradient boosting trees.”
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Digital Twin Enabling Capabilities
Viola, J., & Chen, Y. (2023) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-031-22140-8_3
Wang, Z., Wang, C., & Wang, C. (2023) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100531
A Real-Time Digital Twin to Inform In-Process Decision Support in Additive ManufacturingPreprint
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“Among the pillars of Industry 4.0, Internet of Things (IoT) and Digital Twin (DT) are the rudder of the digital transition, facilitating the real-time collection, processing, and analysis of data to achieve these goals [2].”
BackgroundInfluentialUTILIZING A UNIQUE DEEP LEARNING TECHNIQUE FOR DETECTING ANOMALIES IN INDUSTRIAL AUTOMATION SYSTEMS
Kenchappa, R. M., Yadav, R. K., Singh, A., & Pandey, A. K. (2024) · Proceedings on Engineering Sciences · doi:10.24874/pes.si.24.02.007
“Huang et al., (2021) examined the preventive repair scheduling along with prompt identification of possible manufacturing equipment breakdowns depending on effective identification of anomalies.”
BackgroundDesign of Rule Translator for Temporal Rules Execution of BM-DEVS Model
Ahn, J., & Cho, T. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2447-5_52
Hidden fault diagnosis method of digital twin system for crane
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“In order to ensure the accuracy and integrity of the data, the system also implements data denoising and outlier detection algorithms to eliminate errors caused by environmental interference or equipment problems [13].”
Dahlan, A. F., Cheng, C.-Y., Sae-chai, P., Jientrakul, R., & Yuangyai, C. (2025) · Annals of Operations Research · doi:10.1007/s10479-025-06806-2 · Cited by 1
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Digital Twin System Framework for Large Free Forging Hydraulic Press
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“In references [21-23], combination of digital twin and deep reinforcement learning is applied for intelligent control and fault prediction.”
SINGH, B., Ashima, J. A. I. N., & Kaunert, C. (2025) · doi:10.1002/9781394411320.ch7
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Digital Timetable Automation System
Pawar, A., Aradhye, P., katte, A., & Utpat, V. B. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-29458
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Pawar, A., Aradhye, P., katte, A., & Utpat, V. B. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-29459
Panou, Z., Scivoletto, G., Bernini, G., Siokis, A., Ramantas, K., & Verikoukis, C. V. (2025) · IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) · doi:10.1109/nfv-sdn66355.2025.11349547
“The use of edge computing in automotive applications has been researched to address latency constraints in real-time operations [18].”
Stahmann, P., & Janiesch, C. (2026) · IEEE Access · doi:10.1109/access.2026.3666099 · Cited by 2
“For example, in terms of real-time analysis, digital twins can serve as a source for describing as-is states in addition to the physical machines in production plants (DA3, e.g., [22], [70]).”
Model Following Predictive Control: MFPC - SyncLMKD
Cardoso, F. S., Oliveira, A., Araújo, T. D. S., & Rohrich, R. F. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00263
“…to propose the use of DTs to detect anomalies and perform manufacturing fault diagnostics, [9], while still there is a gap on systemic research field about the full employment of the DT perform accurate diagnosis of faults with standardized data processing and flow in complex systems, [10].”
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Challenges of digital twin applications for radio telescopes
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Menayo, A. C., Sánchez-Herguedas, A., & Márquez, A. C. (2026) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x261463648
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Self-citationFramework and Requirements for Digital Twin-Based Smart Manufacturing
Kamach, O., Ouahabi, N., & Chebak, A. (2026) · Computational intelligence methods and applications · doi:10.1007/978-981-95-5271-9_2
Sakha, R., Venkat, S., Sekaran, R., Vaithiyanathan, R., Jeeva, S., & S, G. G. (2026) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs69077.2026.11634862
“The framework proposed by Huang et al. [5] is an anomaly detection paradigm that is driven by Digital Twin and uses edge intelligence, distributing inference at the physical, edge, and cloud layers.”
VARADE++: An Edge-Friendly Framework for Real-Time Anomaly Detection in Production Plants
Mascolini, A., Gaiardelli, S., Ponzio, F., Dall’Ora, N., Quer, S., Fummi, F., Vinco, S., & Cataldo, S. D. (2026) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2026.3726298
Ma, X., Zhao, Y., Chu, H., & Xu, J. (2026) · International Conference on Computer Information and Big Data Applications (CIBDA) · doi:10.1145/3813822.3814007
“Huang et al. [10] constructed an edge-intelligent DT-based online anomaly detection architecture that integrates historical and real-time data under edge/cloud collaboration to achieve real-time health monitoring and early anomaly identification for industrial systems.”
A Digital Twin Workshop Active Scheduling Method Based on Spatio-Temporal Transformer Prediction
Liu, X., Wang, W., Gao, F., & Yuan, S. (2026) · International Conference on Unmanned System and Intelligent Technology (USIT) · doi:10.1109/usit70862.2026.11682111
“Digital twin technology supports real-time monitoring, simulation, and scheduling optimization by mapping the physical workshop into a virtual space [1], [2].”
Aniba, Y., Benbouhenni, H., Sakouchi, A., Bizon, N., Bouhedda, M., Seddiki, M., Boulesnam, M., & Tulbure, A. A. (2026) · Electronics · doi:10.3390/electronics15194576
Machine Learning and Deep Learning Approaches for Real-Time Anomaly Detection in IoT Systems
Goswami, C., Sheikh, A., Kumar, R., Dhave, S., Deokar, S., & Shrivastava, B. (2026) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11716791
“Real-time detection of anomalies is a key capability for businesses to prevent potential failures before they become costly downtimes, optimize maintenance costs, and improve operational efficiency [37].”
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“In many cases, an intermediate edge layer is employed to support real-time applications by enabling low-latency processing and reducing dependence on cloud communication [18] [19].”
Zhou, Y., Zhou, G., Zhang, C., Wang, Z., Liu, J., & Nandi, A. K. (2026) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2026.101232
Metadata
- Authors
- Huang, H., Yang, L., Wang, Y., Xu, X., & Lu, Y.
- Affiliations
- University of Auckland (NZ)
- State Key Laboratory Fluid Power and Mechatronic Systems (CN)
- Corresponding author
- Wang, Yuanbin
- Lu, Yuqian
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 59
- Pages
- 138-150
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W3134116407
- Cited by
- 183
- References
- 62
- Citations by year
- 2026: 25 · 2025: 36 · 2024: 48 · 2023: 40 · 2022: 22 · 2021: 12
- FWCI
- 15.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Manufacturing Systems (publishedVersion)
- Funders
- China Scholarship Council
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- digital twin, online anomaly detection, edge AI, edge computing, industrial automation systems, real-time health monitoring, anomaly prediction, edge-cloud networks, proactive maintenance, fault detection
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