Cloud-enabled prognosis for manufacturing

被引：249

作者：

Gao, R. ^{[1
]}

Wang, L. ^{[2
]}

Teti, R. ^{[3
]}

Dornfeld, D. ^{[4
]}

Kumara, S. ^{[5
]}

Mori, M. ^{[6
]}

Helu, M. ^{[7
]}

机构：

[1] Case Western Reserve Univ, Dept Mech & Aerosp Engn, Cleveland, OH 44106 USA

[2] KTH Royal Inst Technol, Dept Prod Engn, Stockholm, Sweden

[3] Univ Naples Federico II, Dept Chem Mat & Ind Prod Engn, Naples, Italy

[4] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94720 USA

[5] Penn State Univ, Dept Ind & Mfg Engn, University Pk, PA 16802 USA

[6] DMG Mori Seiki Co Ltd, Nagoya, Aichi, Japan

[7] NIST, Engn Lab, Gaithersburg, MD 20899 USA

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2015年 / 64卷 / 02期

基金：

美国国家科学基金会;

关键词：

Predictive model; Condition monitoring; Cloud manufacturing; REMAINING USEFUL LIFE; CONDITION-BASED MAINTENANCE; SEMI-MARKOV MODEL; ADAPTIVE NEURO-FUZZY; TOOL-WEAR; PARAMETER-ESTIMATION; DEGRADATION MODELS; PARTICLE FILTERS; FAULT-DIAGNOSIS; MONITORING DATA;

D O I：

10.1016/j.cirp.2015.05.011

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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. (C) 2015 CIRP.

引用

页码：749 / 772

页数：24

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