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Cloud-Based Prognosis: Perspective and Challenge

[+] Author Affiliations
Yunjie Yang, Robert X. Gao, Zhaoyan Fan, Jinjiang Wang

University of Connecticut, Storrs, CT

Lihui Wang

KTH Royal Institute of Technology, Stockholm, Sweden

Paper No. MSEC2014-4155, pp. V001T04A028; 7 pages
  • ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference
  • Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing
  • Detroit, Michigan, USA, June 9–13, 2014
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4580-6
  • Copyright © 2014 by ASME


Comprehensive acquisition, distribution, and utilization of information about machine equipment and/or processes across spatial boundaries for improved productivity and decision have increasingly become the hallmark of advanced manufacturing. The emergence of cloud computing has created ample opportunities to achieve this goal. This paper presents a review of the state-of-the-art of prognosis technique for manufacturing and its future development motivated by the cloud infrastructure. Prevailing methods of prognosis are summarized, and their respective performance is comparatively evaluated. Basic principles and recent advances in cloud computing, as well as its application to cloud manufacturing, are introduced. Based on the survey, the concept of cloud-based prognosis is proposed, and its architecture as well as associated challenges are discussed.

Copyright © 2014 by ASME



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