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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753
Self-citationJia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579
Fault Detection and Diagnosis in Industrial Systems
Chiang, E. L. R. A. R. D. B. L. H. (2001) · Measurement Science and Technology · doi:10.1088/0957-0233/12/10/706 · Cited by 1,424
A review on condition-based maintenance optimization models for stochastically deteriorating system
Alaswad, S., & Xiang, Y. (2016) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2016.08.009 · Cited by 800
Digital Twin for rotating machinery fault diagnosis in smart manufacturing
Wang, J., Ye, L., Gao, R. X., Li, C., & Zhang, L. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1552032 · Cited by 552
A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning
Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019) · IEEE Access · doi:10.1109/access.2018.2890566 · Cited by 533
Huang, R., Xi, L., Li, X., Liu, C., Qiu, H., & Lee, J. (2006) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2005.11.008 · Cited by 523
New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics
Tao, F., & Qi, Q. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2723764 · Cited by 434
Research works printed but not linked: 1
- Noureddine, 2012, Surface roughness measurement for model-based fault detection in turning process, Int Rev Mech Eng, 6, 1411
Cited by
Sorted by citations
Design, Modeling and Implementation of Digital Twins
Segovia, M., & García-Alfaro, J. (2022) · Sensors · doi:10.3390/s22145396 · Cited by 355
“Huang et al. [30] propose an anomaly detection framework with real-time monitoring for industrial systems.”
Edge Computing on IoT for Machine Signal Processing and Fault Diagnosis: A Review
Lu, S., Lu, J., An, K., Wang, X., & He, Q. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3239944 · Cited by 306
“The gradient boosting decision tree regression method was implemented on collaborated edge–cloud computing framework in [109].”
MethodSindhwani, R., Afridi, S., Kumar, A., V, Banaitis, A., Luthra, S., & Singh, P. L. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.101887 · Cited by 285
Murtaza, A. A., Saher, A., Zafar, M. H., Moosavi, S. K. R., Aftab, M. F., & Sanfilippo, F. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102935 · Cited by 244
A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems
Jeffrey, N., Tan, Q., & Villar, J. R. (2023) · Electronics · doi:10.3390/electronics12153283 · Cited by 160
Wang, K., Hu, Q., Zhou, M., Zun, Z., & Qian, X. (2021) · Case Studies on Transport Policy · doi:10.1016/j.cstp.2021.06.014 · Cited by 127
Agrawal, S., Agrawal, R., Kumar, A., V, Luthra, S., & Garza‐Reyes, J. A. (2023) · Operations Management Research · doi:10.1007/s12063-023-00410-y · Cited by 140
“0 (Huang et al. 2021).”
Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition
Deebak, B. D., & Al-Turjman, F. M. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22493 · Cited by 110
“80 In the diagnosis of DNN ‐ Virtual, the maximum accuracy is recorded as ~90.03% , whereas the average accuracy is just ~86.74% .”
BackgroundInfluentialA Comprehensive Survey of Digital Twins in Healthcare in the Era of Metaverse
Turab, M., & Jamil, S. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3030039 · Cited by 100
Dong, Y., Jiang, H., Wu, Z., Yang, Q., & Liu, Y. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109253 · Cited by 93
Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest
Guo, K. Z., Wan, X., Liu, L., Gao, Z., & Yang, M. (2021) · Applied Sciences · doi:10.3390/app11167733 · Cited by 101
“It includes a geometric model, a function model, and an information model [38].”
MethodAdvances of Digital Twins for Predictive Maintenance
You, Y., Chen, C., Hu, F., Liu, Y., & Ji, Z. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.348 · Cited by 98
Deep Video Anomaly Detection: Opportunities and Challenges
Ren, J., Xia, F., Liu, Y., & Lee, I. (2021) · 2021 International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw53433.2021.00125 · Cited by 84
Benedictis, A. D., Flammini, F., Mazzocca, N., Somma, A., & Vitale, F. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3246983 · Cited by 85
Latsou, C., Farsi, M., & Erkoyuncu, J. A. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.02.008 · Cited by 80
Artificial intelligence and edge computing for machine maintenance-review
Bala, A., Rashid, R. Z. J. A., Ismail, I. B., Oliva, D. A., Muhammad, N., Sait, S. M., Al-Utaibi, K. A., Amosa, T. I., & Memon, K. A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10748-9 · Cited by 90
“119 Page 14 of 33 Furthermore, Huang et al. (2021) developed an edge AI-based predictive maintenance method.”
InfluentialDigital Twin-based manufacturing system: a survey based on a novel reference model
Liu, S., Zheng, P., & Bao, J. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02172-7 · Cited by 84
“…design evaluation (He et al., 2021a), system design (Wang et al., 2021), process status monitoring (Polini & Cor-rado, 2021), energy consumption evaluation (Lu et al., 2019), industrial system monitoring (Calvo-Bascones et al., 2023; Huang et al., 2021), system maintenance (Mi et al., 2021), etc.”
Krupáš, M., Kajáti, E., Liu, C., & Zolotová, I. (2024) · Sensors · doi:10.3390/s24072232 · Cited by 78
“…anomaly detection [97] Human safety [55,75,80] Productivity [55] Human skill level analysis [50] Dynamic changes’ prediction [73] Maintenance [97] Analysis Principal component analysis [50] Parameter sensitivity analysis [105] Dimension reduction [50] Model adaptability enhancement [105]…”
BackgroundInfluentialKarkaria, V. N., Tsai, Y.-K., Chen, Y.-P., & Chen, W. (2025) · Engineering Optimization · doi:10.1080/0305215x.2024.2434201 · Cited by 78
“Moreover, adaptive learning mechanisms enhance the robustness of digital twins by allowing them to learn from anomalies and integrate this learning into future operations (H. Huang et al .”
Digital twin-driven fault diagnosis for CNC machine tool
Xue, R., Zhang, P., Huang, Z., & Wang, J. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09978-4 · Cited by 71
“As mentioned above, fault diagnosis methods can be organized into three categories: knowledge-based methods, model-based methods and data-driven methods [4].”
Huang, K., Tao, Z., Wang, C., Guo, T., Yang, C., & Gui, W. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3161640 · Cited by 68
“The cloud-edge collaboration framework realizes the complementary advantages of edge intelligence and cloud computing [19], [20].”
BackgroundRapid construction method of equipment model for discrete manufacturing digital twin workshop system
Zhang, Y., Zhang, C., Yan, J., Yang, C., & Liu, Z. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102309 · Cited by 64
Gültekın, Ö., Çinar, E. M., Özkan, K., & Yazıcı, A. T. (2022) · Sensors · doi:10.3390/s22093208 · Cited by 68
The Role of AI in Warehouse Digital Twins: Literature Review
Elbouzidi, A. D., Cadi, A. A. E., Pellerin, R., Lamouri, S., Valencia, E. T., & Bélanger, M.-J. (2023) · Applied Sciences · doi:10.3390/app13116746 · Cited by 78
A unified framework for digital twin development in manufacturing
Latsou, C., Ariansyah, D., Salome, L., Erkoyuncu, J. A., Sibson, J., & Dunville, J. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102567 · Cited by 70
Chen, S., Bekar, E. T., Bokrantz, J., & Skoogh, A. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.07.006 · Cited by 87
Time Series Anomaly Detection in Power Electronics Signals with Recurrent and Convlstm Autoencoders
Radaideh, M. I., Pappas, C. H., Walden, J., Lu, D., Vidyaratne, L., Britton, T., Rajput, K., Schram, M., & Cousineau, S. M. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103704 · Cited by 65
De Kooning, J. D. M., Stockman, K., De Maeyer, J., Jarquin-Laguna, A., & Vandevelde, L. (2021) · Processes · doi:10.3390/pr9122224 · Cited by 57
“…the digital twin is used for condition monitoring, a shut down signal can be sent to the physical system upon the detection of an anomaly that causes a safety concern [28], or a warning can be sent to the turbine operator to signal an upcoming component failure as a predictive maintenance measure.”
MethodGRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks
Singh, S. K., Kumar, M., Tanwar, S., & Park, J. H. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.12.009 · Cited by 57
Developing and leveraging digital twins in engineering design
Anwer, N., Stark, R., Tao, F., & Erkoyuncu, J. A. (2025) · CIRP Annals · doi:10.1016/j.cirp.2025.05.002 · Cited by 58
Network Digital Twin for 6G and Beyond: An End-to-End View Across Multi-Domain Network Ecosystems
Tran, D.-H., Waheed, N., Saputra, Y. M., Lin, X., Nguyen, C. T., Abdu, T. S., Vo, V. N., Pham, V., Alsenwi, M., Adam, A. B. M., et al. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3599866 · Cited by 61
“Similarly, [93] presents a new framework that uses DT technology to detect anomalies in industrial systems in real time and even predict them.”
Lal, A., Dang, J., Nabzdyk, C. S., Gajic, O., & Herasevich, V. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-4203 · Cited by 53
“Outside of the world of medicine and healthcare, the concept of the digital twin has been increasingly pervasive and has been extensively used all the way from modeling of smart cities, anomaly detection in the automobile industry to the design of electrical systems (12-14).”
MethodFeng, X., Wu, J., Wu, Y., Li, J., & Yang, W. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119169 · Cited by 62
Digital Twin-Enabled Service Provisioning in Edge Computing via Continual Learning
Li, J., Guo, S., Liang, W., Wang, J., Chen, Q., Zeng, Y. W., Ye, B., & Jia, X. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3332668 · Cited by 51
“In the sequel, edge intelligence has gained mounting traction in boosting the performance of digital twins and endowing high consistency between digital twins and their physical objects via edge devices [9].”
BackgroundDigital Twin for Railway: A Comprehensive Survey
Ghaboura, S., Ferdousi, R., Laamarti, F., Yang, C., & Saddik, A. E. (2023) · IEEE Access · doi:10.1109/access.2023.3327042 · Cited by 48
Digital twin enabled transition towards the smart electric vehicle charging infrastructure: A review
Yu, G., Ye, X., Xia, X., & Chen, Y. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105479 · Cited by 54
Bi, Z., Zhang, W., Wu, C., & Li, L. (2021) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100316 · Cited by 47
García, Á., Bregón, A., & Martínez‐Prieto, M. A. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101094 · Cited by 48
Stormwater digital twin with online quality control detects urban flood hazards under uncertainty
Kim, Y., Oh, J., & Bartos, M. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105982 · Cited by 45
A digital twin dosing system for iron reverse flotation
Zhang, D., & Gao, X. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.03.006 · Cited by 46
DTUMOS, digital twin for large-scale urban mobility operating system
Yeon, H., Eom, T., Jang, K., & Yeo, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-32326-9 · Cited by 44
Digital Twin for Flexible Manufacturing Systems and Optimization Through Simulation: A Case Study
Florescu, A. (2024) · Machines · doi:10.3390/machines12110785 · Cited by 50
Calvo-Bascones, P., Voisin, A., Do, P., & Sanz-Bobi, M. A. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103767 · Cited by 45
Digital twin-assisted intelligent anomaly detection system for Internet of Things
Bolat-Akça, B., & Bozkaya, E. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103484 · Cited by 45
Digital twin model for chiller fault diagnosis based on SSAE and transfer learning
Ma, X., Chen, F., Wang, Z., Li, K., & Tian, C. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110718 · Cited by 41
Health monitoring of CNC machining processes using machine learning and wavelet packet transform
Hussain, A., Janjua, T. A. M., Malik, A. N., Najib, A., & Khan, S. A. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111326 · Cited by 40
Monitoring industrial control systems via spatio-temporal graph neural networks
Wang, Y., Peng, H., Wang, G., Tang, X., Wang, X., & Liu, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106144 · Cited by 38
Self-citationChen, S., Xiao, X., Li, Z., & Ouyang, L. (2025) · Materials Today · doi:10.1016/j.mattod.2025.09.016 · Cited by 39
Digital twins for the designs of systems: a perspective
van Beek, A., Karkaria, V. N., & Chen, W. (2022) · Structural And Multidisciplinary Optimization · doi:10.1007/s00158-023-03488-x · Cited by 38
“This capability can be further enhanced through uncertainty quantification to balance the need for exploitation with exploration [54].”
Dissecting the Impact of Information and Communication Technologies on Digital Twins as a Service
Groshev, M., Guimarães, C., de la Oliva, A., & Gazda, R. (2021) · IEEE Access · doi:10.1109/access.2021.3098109 · Cited by 35
“Edge-based Digital Twins have also been studied in the literature with their benefits for manufacturing such as anomaly detection for automation systems [39] and improving production quality, efficiency and costs for metal additive”
MethodPathirannahalage, I., Jayasooriya, V., Samarabandu, J. K., & Akila Subasinghe, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19204-w · Cited by 35
Anomaly detection in smart manufacturing: An Adaptive Adversarial Transformer-based model
Orabi, M., Tran, K. P., Egger, P., & Thomassey, S. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.09.021 · Cited by 35
Gupta, R., Tian, B., Wang, Y., & Nahrstedt, K. (2024) · Future Internet · doi:10.3390/fi16070239 · Cited by 33
Self-citationAIDA—A holistic AI-driven networking and processing framework for industrial IoT applications
Chahed, H., Usman, M., Chatterjee, A., Bayram, F., Chaudhary, R., Brunström, A., Taheri, J., Ahmed, B. S., & Kassler, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100805 · Cited by 30
A survey on security enhancing Digital Twins: Models, applications and tools
Qureshi, A., Asensio, A., Imran, M. A., García, J., & Masip‐Bruin, X. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108158 · Cited by 33
“In the recent years, several works have been published on modeling techniques [17,21,24,25,47].”
Automatic generation of structural geometric digital twins from point clouds
Mirzaei, K., Arashpour, M., Asadi, E., Masoumi, H., & Li, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-26307-7 · Cited by 31
Process Mining for Digital Twin Development of Industrial Cyber-Physical Systems
Vitale, F., Guarino, S., Flammini, F., Faramondi, L., Mazzocca, N., & Setola, R. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3465600 · Cited by 32
Milling surface roughness monitoring using real-time tool wear data
Wang, R., Song, Q., Peng, Y., Liu, Z., Ma, H., Liu, Z., & Xu, X. W. (2024) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2024.109821 · Cited by 29
Self-citationIndustrial applications of digital twins: A systematic investigation based on bibliometric analysis
Ren, J., Ahmad, R., Li, D., Ma, Y., & Hui, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103264 · Cited by 32
Digital Twin Framework for Lathe Tool Condition Monitoring in Machining of Aluminium 5052
GaneshKumar, S., Singh, B., Kumar, R. S., & Haldorai, A. (2023) · Defence Science Journal · doi:10.14429/dsj.73.18650 · Cited by 22
“Hence the data is recorded concerning the machining time 20-26 .”
MethodA Review of Digital Twinning for Rotating Machinery
Inturi, V., Ghosh, B., Sabareesh, G. R., & Pakrashi, V. (2024) · Sensors · doi:10.3390/s24155002 · Cited by 33
Zhang, D., Liu, Z., Li, F., Zhao, Y., Zhang, C., Li, X., & Zhang, Y. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102600 · Cited by 27
Real-world prototyping and evaluation of digital twins for predictive facility maintenance
Asare, K. A. B., Liu, R., Anumba, C., & Issa, R. R. A. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110890 · Cited by 24
FPGA-Based Digital Twin Implementation for Power Converter System Monitoring
Nwoke, J., Milanesi, M., Viola, J., & Chen, Y. (2023) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi59677.2023.10365466 · Cited by 24
An industrial edge computing architecture for Local Digital Twin
Kondo, R. E., Andrade, W. J., Henequim, C. D. M., Lazzaretti, A. E., Britto, A. D. S., Loures, E. D. F. R., Santos, E. A. P., & Reynoso-Meza, G. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110257 · Cited by 28
Webb, L., Tokhi, O., & Alkan, B. (2024) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2024.2387775 · Cited by 25
“Thirdly, edge computing has gained significance, enabling local data processing for reduced latency and improved decision-making (Huang et al. 2021).”
Sun, X., Liu, S., Bao, J., Li, J., & Liu, Z. (2021) · Machines · doi:10.3390/machines10010011 · Cited by 21
“Therefore, to reduce the data transmission delay and retain the high-performance computing of digital twin assembly, the use of edge computing is an effective solution to realize fast processing and flexible computing of twin data [9].”
BackgroundMethodNetwork Digital Twins: A Systematic Review
Verdecchia, R., Scommegna, L., Picano, B., Becattini, M., & Vicario, E. (2024) · IEEE Access · doi:10.1109/access.2024.3453034 · Cited by 24
A Novel Online Machine Learning Approach for Real-Time Condition Monitoring of Rotating Machines
Mostafavi, A., & Sadighi, A. (2021) · 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM) · doi:10.1109/icrom54204.2021.9663495 · Cited by 20
A matrix analytic approach for Bayesian network modeling and inference of a manufacturing system
Zhang, D., Liu, Q., Yan, H., & Xie, M. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.016 · Cited by 18
Xia, W., Liu, X., Yue, C., Li, H., Li, R., & Wei, X. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11016-w · Cited by 18
“Physical space and digital space interact with information and knowledge data through data management system [33].”
BackgroundZhu, L., Wang, C., Zhihui, H., & Zhang, Y. (2021) · World Wide Web · doi:10.1007/s11280-021-00983-3 · Cited by 17
“Huang et al. [18] adopted the edge AI philosophy distributing supervised machine learning, data processing and decision-making tasks among the physical layer, edge layer and cloud layer.”
MethodLi, Z., Zhao, Y., Zhang, Y., Li, X., & Bu, L. (2024) · Journal of Engineering Design · doi:10.1080/09544828.2024.2332122 · Cited by 17
“It incorporates data from physical entities and digital models, providing accurate information for anomaly detection when sufficient data is available (Huang et al. 2021; Liu et al. 2023).”
Siaterlis, G., Franke, M., Klein, K. M., Hribernik, K., Papapanagiotakis, G., Palaiologos, S., Antypas, G., Nikolakis, N., & Alexopoulos, K. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.02.192 · Cited by 16
Causality-guided counterfactual debiasing for anomaly detection of cyber-physical systems
Tang, W., Liu, J., Zhou, Y., & Ding, Z. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3326544 · Cited by 16
An edge-based algorithm for tool wear monitoring in repetitive milling processes
Liu, R. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01925-0 · Cited by 15
“Although there have been some developments to realize edge com - puting in TCM systems [38, 39], most of the proposed architectures still lack the practical applicability in actual production scenarios, especially for small and medium-sized manufacturers.”
BackgroundNwoke, J., Milanesi, M., Viola, J., Chen, Y., & Visioli, A. (2024) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2024.3404563 · Cited by 15
“To address these challenges, alternative approaches, such as edge computing-based or cloud-edge-based digital twin implementations [17], [18], [19], have been explored.”
MethodDigital Twin-assisted anomaly detection for industrial scenarios
Alcaraz, C., & López, J. (2024) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2024.100721 · Cited by 18
Xu, R., Park, C., Khan, S., Jin, W., Moe, S. J. S., & Kim, D. H. (2023) · IEEE Access · doi:10.1109/access.2023.3325475 · Cited by 14
A Deep Trajectory Controller for a Mechanical Linear Stage Using Digital Twin Concept
Chaiprabha, K., & Chancharoen, R. (2023) · Actuators · doi:10.3390/act12020091 · Cited by 14
An IoT Architecture Leveraging Digital Twins: Compromised Node Detection Scenario
Alanezi, K., & Mishra, S. (2024) · IEEE Systems Journal · doi:10.1109/jsyst.2024.3403500 · Cited by 15
Nayak, R., Pati, U. C., & Das, S. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17889-z · Cited by 12
An adaptable Digital Twin model for manufacturing
Huang, H., Ji, T., & Xu, X. W. (2024) · Manufacturing Letters · doi:10.1016/j.mfglet.2024.09.142 · Cited by 12
Self-citationStahmann, P., Nebel, M., & Janiesch, C. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111236 · Cited by 12
A Deployable Edge Computing Solution for Machine Condition Monitoring
Zhao, X., & Wang, P. E. (2024) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc60896.2024.10560795 · Cited by 11
Digital Twin-Driven VCTS Control: An Iterative Apporach Using Model-Based Reinforcement Learning
Ye, Z., Zhu, L., Li, Y., Wang, H., Yu, F. R., & Tang, T. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3492183 · Cited by 11
Nair, A. J., Manohar, S., & Chaudhry, R. (2024) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-708-320241013 · Cited by 11
How digital twin technology may improve safety management: A multi-industry perspective
Zou, P. X. W., & Ma, S. (2025) · Safety Science · doi:10.1016/j.ssci.2025.106837 · Cited by 13
Multi-modal digital twins for industrial anomaly detection: Framework, method, and application
Ren, C., Li, M., Chen, C., Guan, X., & Huang, G. Q. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103068 · Cited by 11
Physics-based modelling of robot’s gearbox including non-linear phenomena
Aivaliotis, P., Kaliakatsos-Georgopoulos, D., & Makris, S. (2023) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2022.2162594 · Cited by 11
“Also, they are used in Predictive Maintenance (PdM) and Proactive Maintenance (Huang et al. 2021) aiming to evaluate the machinery health status, detecting system anomalies, and scheduling the maintenance processes (Aivaliotis et al. 2019; Aivaliotis, Georgoulias, and Chryssolouris 2019).”
MethodAngelova, R. A. (2025) · Textiles · doi:10.3390/textiles5040049 · Cited by 10
“Implementing a DT with built-in cybersecurity requires the development of a secure infrastructure that includes data encryption [99], access authentication [100], real-time activity logging and monitoring [101], as well as automatic anomaly detection [102].”
A framework for digital twin integration in biofabrication and a scaffold 3D bioplotting case study
Ahmed, N., Narayanan, L. K., & Shirwaiker, R. A. (2024) · Manufacturing Letters · doi:10.1016/j.mfglet.2024.09.144 · Cited by 13
Singh, T. M., & Lippert, K. J. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-5893-1.ch012 · Cited by 9
Acies-OS: A Content-Centric Platform for Edge AI Twinning and Orchestration
Li, J., Chen, Y., Kimura, T., Wang, T., Wang, R., Kara, D., Hu, Y. C., Li, W., Hanafy, W. A., Souza, A., et al. (2024) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn61486.2024.10637580 · Cited by 9
Resource Requirements of an Edge-based Digital Twin Service: An Experimental Study
Mungari, F., Groshev, M., & Chiasserini, C. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.05.005 · Cited by 8
Pervasive and Connected Digital Twins for Edge Computing Enabled Industrial Applications
Sanfilippo, F., Langås, E. F., Bukhari, H. Z., & Robstad, S. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.821 · Cited by 8
Li, J., Dong, H., Long, W., Wang, P., & Wang, X. (2023) · Ships and Offshore Structures · doi:10.1080/17445302.2023.2238391 · Cited by 7
“Subsequently, a popular de fi nition of DT emerged as ‘ a Digital Twin is the digital representation of a physical entity, enabling the convergence between the physical entity and digital representation at an appropriate synchronisation rate ’ (Huang, Yang, Wang, Xu, & Lu, 2021).”
Ocean observing time-series anomaly detection based on DTW-TRSAX method
Wang, Y., Lyu, X., & Yang, S. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06183-w · Cited by 7
Self-citationWang, Q., Zhang, X., Lu, J., Xiao, G., Ren, Y., & Li, W. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3558806 · Cited by 6
“This is attributed to the advancement of computers, communications, sensors, and many other related technologies [21], [22], [23].”
Huang, H., Xu, X. W., & Ji, T. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02634-0 · Cited by 6
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
