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Cloud-Enabled Prognosis for Manufacturing



Robert Gao, Lihui Wang, Roberto Teti, David Dornfeld, Soundar Kumara, Masahiko Mori, Moneer Helu


Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries for improved accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud- enabled prognosis for manufacturing.
CIRP Annals-ManufacturingTechnology


Predictive model, Condition monitoring, Cloud manufacturing


Gao, R. , Wang, L. , Teti, R. , Dornfeld, D. , Kumara, S. , Mori, M. and Helu, M. (2015), Cloud-Enabled Prognosis for Manufacturing, CIRP Annals-ManufacturingTechnology, [online],, (Accessed April 15, 2024)
Created June 21, 2015, Updated October 12, 2021