TY - JOUR
T1 - Cloud-enabled prognosis for manufacturing
AU - Gao, R.
AU - Wang, L.
AU - Teti, R.
AU - Dornfeld, D.
AU - Kumara, S.
AU - Mori, M.
AU - Helu, M.
N1 - Funding Information:
The authors greatly appreciate contribution to this paper by Mr. Peng Wang and graphic support by Dr. Zhaoyan Fan. This work has been partially supported by the US National Science Foundation under awards CMMI-1300999 and CCF-1331850 . Identification of certain commercial systems in this paper does not imply recommendation or endorsement by NIST. Nor does it imply that the products identified are necessarily the best available for the purpose.
Publisher Copyright:
© 2015 CIRP.
PY - 2015
Y1 - 2015
N2 - Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve 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.
AB - Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve 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.
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U2 - 10.1016/j.cirp.2015.05.011
DO - 10.1016/j.cirp.2015.05.011
M3 - Article
AN - SCOPUS:84940450168
VL - 64
SP - 749
EP - 772
JO - CIRP Annals - Manufacturing Technology
JF - CIRP Annals - Manufacturing Technology
SN - 0007-8506
IS - 2
ER -