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GUEST EDITORIAL Special Section: AI in Equipment Service
 

Summary: GUEST EDITORIAL
Special Section: AI in Equipment Service
ALICE AGOGINO1
, PIERO BONISSONE2
, KAI GOEBEL2
, AND GEORGE VACHTSEVANOS3
1
Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94720
2
GE Corporate Research & Development, Information Systems Lab, One Research Circle, Niskayuna, NY 12309
3
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250
This issue of AIEDAM focuses on AI in equipment service. Recently there has been a strong and renewed emphasis on AI
technologies that can be used to monitor products and processes; detect incipient failures; identify possible faults (in various
stages of development); determine preventive or corrective action; generate a cost-efficient repair plan and monitor its execution.
This renewed emphasis stems from a focus of manufacturing companies on the service market where they hope to grow their
market share by offering their customers novel and aggressive service contracts. This service market includes power generation
equipment, aircraft engines, medical imaging systems, and locomotives, just to name a few. In some of these new service
offerings the old parts and labor billing model is replaced by guaranteed uptime. This in turn places the motivation to maintain
equipment in working order on the servicing company. Monitoring can be more efficiently accomplished, in part, by employing

  

Source: Agogino, Alice M. - Department of Mechanical Engineering, University of California at Berkeley

 

Collections: Engineering