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
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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
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.
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