Cited by 183Digital Transformation in IndustryFault Detection and Control SystemsIoT and Edge/Fog Computing

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Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Huang, Huiyue, Yang, Lei, Wang, Yuanbin, Xu, Xun, Lu, Yuqian·2021·Journal of Manufacturing Systems

DOI 10.1016/j.jmsy.2021.02.010

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See all 183

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
ORCID: Huang, Huiyue
Wang, Yuanbin
Xu, Xun
Lu, Yuqian
Venue: Journal of Manufacturing Systems
Publisher: Elsevier BV
Published: 2021-04
Volume: 59
Pages: 138-150
ISSN: 0278-6125 (ISSN-L)
DOI: 10.1016/j.jmsy.2021.02.010
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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