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Special Issue: AI in equipment service

Published online by Cambridge University Press:  11 January 2002

ALICE AGOGINO
Affiliation:
Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94720, USA
PIERO BONISSONE
Affiliation:
GE Corporate Research & Development, Information Systems Lab, Niskayuna, NY 12309, USA
KAI GOEBEL
Affiliation:
GE Corporate Research & Development, Information Systems Lab, Niskayuna, NY 12309, USA
GEORGE VACHTSEVANOS
Affiliation:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA
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Abstract

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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, and 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 remotely monitored systems. Big strides have been taken for in-use monitoring of stationary equipment, such as manufacturing plants or high-end appliances, and also mobile systems such as transportation systems (vehicles, aircraft, locomotives, etc.). While advances in hardware development make it possible to perform these tasks efficiently, there are new avenues for progress in accompanying AI software techniques. Some of these approaches have their roots in efforts of years past while others arise from new challenges. Characteristics of typical challenges for AI in monitoring and diagnosis (M&D) service can be categorized into input, model, and output. In particular, input questions try

Type
GUEST EDITORIAL
Copyright
© 2001 Cambridge University Press