Cloud-enabled prognosis for manufacturing
By Robert X. Gao, Lihui Wang, Roberto Teti, David A. Dornfeld, Soundar Kumara *, M. Mori, Moneer Helu. 2015
315 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/340b35a69467b81493b7484bb75366bd13102291
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In Industrial and Manufacturing Engineering
Citations in the corpus
- Predictive Maintenance and Intelligent Sensors in Smart Factory: Review (2021, background): “The most significant publications of this period focused on big data analytics in logistics and supply chain management [85], maintenance strategy selection [86], vibration analysis of rotating machinery, or cloud-enabled prognosis [87] for predictive maintenance in production.”
- Gesture recognition for human-robot collaboration: A review (2017, background): “Recently, several band-based sensor gesture control systems have been reported (Zhang and Harrison, 2015; Haroon and Malik, 2016; Roy et al., 2016).”
- Cloud-enhanced predictive maintenance (2016, background): “The appropriate means to synthesise data in this way remains an open research question [27].”
- A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises (2016, background): “Recent concepts such as the Internet of Things, Industrial Internet, Cloud-based Manufacturing [7] and Smart Manufacturing address these requirements in part and are commonly subsumed by the visionary concept of a Fourth Industrial Revolution (Industry 4.0) [8].”
- Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production (2016, background): “Recent concepts such as the Internet of Things, Industrial Internet, Cloud-based Manufacturing [4] and Smart Manufacturing address this vision of future digitally enabled production and are commonly subsumed by the visionary concept of a Fourth Industrial Revolution or Industry 4.”
- New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics (2019, background): “Moreover, CPS can be also implemented in the cloud environment for the cloud-enabled prognosis, remote accessibility, and controllability of machines and processes across spatial boundaries [47], [48].”
- Digital Twin for rotating machinery fault diagnosis in smart manufacturing (2018, background): “Advances in sensor and communication technologies provide foundations for linking the physical world of machines to the cyber world of computation (Monostori et al. 2016; Wang et al. 2015a; Gao et al. 2015; Wu et al.2017; Wang et al. 2017b).”
- Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, unknown): “…sees the ML landscape transformed into deep learning (DL), which leverages advanced computational infrastructure, such as cloud computing [96,97], and cascade of network layers that corresponds to different levels of abstraction of machine condition-related patterns [5], thereby improving…”