Journal ArticleDOI
A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing
Dazhong Wu,Shaopeng Liu,Li Zhang,Janis P. Terpenny,Robert X. Gao,Thomas R. Kurfess,Judith Ann Guzzo +6 more
TLDR
A new computational framework that enables remote real-time sensing, monitoring, and scalable high performance computing for diagnosis and prognosis is introduced and a proof-of-concept prototype is developed to demonstrate how the framework can enable manufacturers to monitor machine health conditions and generate predictive analytics.About:
This article is published in Journal of Manufacturing Systems.The article was published on 2017-04-01. It has received 223 citations till now. The article focuses on the topics: Prognostics & Wireless sensor network.read more
Citations
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Journal ArticleDOI
Deep learning for smart manufacturing: Methods and applications
TL;DR: A comprehensive survey of commonly used deep learning algorithms and discusses their applications toward making manufacturing “smart”, including computational methods based on deep learning that aim to improve system performance in manufacturing.
Journal ArticleDOI
Data-driven smart manufacturing
TL;DR: The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.
Journal ArticleDOI
A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs)
TL;DR: In this paper, the authors present features that are characteristic for SMEs and identify research gaps needed to be addressed to successfully support manufacturing SMEs in their progress towards Industry 4.0.
Journal ArticleDOI
Enabling technologies and tools for digital twin
TL;DR: 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin, and the frequently-used enabling technologies and tools for digital twin are investigated and summarized to provide Technologies and tools references for the applications of digital twin in the future.
Journal ArticleDOI
Enabling technologies for fog computing in healthcare IoT systems
Ammar Awad Mutlag,Mohd Khanapi Abd Ghani,N. Arunkumar,Mazin Abed Mohammed,Mazin Abed Mohammed,Othman Mohd +5 more
TL;DR: A systematic literature review of the technologies for fog computing in the healthcare IoT systems field and analyzing the previous is presented, providing motivation, limitations faced by researchers, and suggestions proposed to analysts for improving this essential research field.
References
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Proceedings ArticleDOI
Fog computing: A cloud to the ground support for smart things and machine-to-machine networks
TL;DR: This survey article expands fog computing concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios.
Journal ArticleDOI
An interoperable solution for Cloud manufacturing
Xi Vincent Wang,Xun Xu +1 more
TL;DR: This study presents an interoperable manufacturing perspective based on Cloud manufacturing, a service-oriented, interoperable Cloud manufacturing system that brings into manufacturing industry with a number of benefits such as openness, cost-efficiency, resource sharing and production scalability.
Journal ArticleDOI
Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring
S.S. Rangwala,David Dornfeld +1 more
Proceedings ArticleDOI
Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud
TL;DR: Experiments conducted in a local file setting provide evidence that this approach to securing data in the cloud using offensive decoy technology may provide unprecedented levels of user data security in a Cloud environment.
Journal ArticleDOI
Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach
TL;DR: In this article, a methodology for diagnostics of fixture failures in multistage manufacturing processes (MMP) is presented, which is based on the state-space model of the MMP process, which includes part fixturing layout geometry and sensor location.