TY - JOUR
T1 - Intelligent prognostics tools and e-maintenance
AU - Lee, Jay
AU - Ni, Jun
AU - Djurdjanovic, Dragan
AU - Qiu, Hai
AU - Liao, Haitao
N1 - Funding Information:
This research is supported by the NSF Industry/University Cooperative Research Center (I/UCRC) for Intelligent Maintenance Systems (IMS) at the University of Cincinnati and University of Michigan.
PY - 2006/8
Y1 - 2006/8
N2 - In today's global competitive marketplace, there is intense pressure for manufacturing industries to continuously reduce and eliminate costly, unscheduled downtime and unexpected breakdowns. With the advent of Internet and tether-free technologies, companies necessitate dramatic changes in transforming traditional "fail and fix (FAF)" maintenance practices to a "predict and prevent (PAP)" e-maintenance methodology. E-maintenance addresses the fundamental needs of predictive intelligence tools to monitor the degradation rather than detecting the faults in a networked environment and, ultimately to optimize asset utilization in the facility. This paper introduces the emerging field of e-maintenance and its critical elements. Furthermore, performance assessment and prediction tools are introduced for continuous assessment and prediction of a particular product's performance, ultimately enable proactive maintenance to prevent machine from breakdowns. Recent advances on intelligent prognostic technologies and tools are discussed. Several case studies are introduced to validate these developed technologies and tools.
AB - In today's global competitive marketplace, there is intense pressure for manufacturing industries to continuously reduce and eliminate costly, unscheduled downtime and unexpected breakdowns. With the advent of Internet and tether-free technologies, companies necessitate dramatic changes in transforming traditional "fail and fix (FAF)" maintenance practices to a "predict and prevent (PAP)" e-maintenance methodology. E-maintenance addresses the fundamental needs of predictive intelligence tools to monitor the degradation rather than detecting the faults in a networked environment and, ultimately to optimize asset utilization in the facility. This paper introduces the emerging field of e-maintenance and its critical elements. Furthermore, performance assessment and prediction tools are introduced for continuous assessment and prediction of a particular product's performance, ultimately enable proactive maintenance to prevent machine from breakdowns. Recent advances on intelligent prognostic technologies and tools are discussed. Several case studies are introduced to validate these developed technologies and tools.
KW - E-maintenance
KW - Predictive maintenance
KW - Prognostics
KW - Remote monitoring
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U2 - 10.1016/j.compind.2006.02.014
DO - 10.1016/j.compind.2006.02.014
M3 - Article
AN - SCOPUS:33746216902
SN - 0166-3615
VL - 57
SP - 476
EP - 489
JO - Computers in Industry
JF - Computers in Industry
IS - 6
ER -