Intelligent prognostics tools and e-maintenance

Authors:
Jay Lee;Jun Ni;Dragan Djurdjanovic;Hai Qiu;Haitao Liao
Affiliations:
NSF Center for Intelligent Maintenance System, University of Cincinnati;NSF Center for Intelligent Maintenance System, University of Michigan;NSF Center for Intelligent Maintenance System, University of Michigan;NSF Center for Intelligent Maintenance System, University of Cincinnati;Wichita University
Venue:
Computers in Industry - Special issue: E-maintenance
Year:
2006

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Abstract

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.