DocumentCode :
1057237
Title :
Fault Detection, Diagnostics, and Prognostics: Software Agent Solutions
Author :
Liu, Li ; Logan, Kevin P. ; Cartes, David A. ; Srivastava, Sanjeev K.
Author_Institution :
Center for Adv. Power Syst.-Florida State Univ., Tallahassee
Volume :
56
Issue :
4
fYear :
2007
fDate :
7/1/2007 12:00:00 AM
Firstpage :
1613
Lastpage :
1622
Abstract :
Fault diagnosis and prognosis are important tools for the reliability, availability, and survivability of navy all-electric ships (AES). Extending the fault detection and diagnosis into predictive maintenance increases the value of this technology. The traditional diagnosis can be viewed as a single diagnostic agent having a model of the component or the whole system to be diagnosed. This becomes inadequate when the components or system become large, complex, and even distributed as on navy electric ships. For such systems, the software multiagents may offer a solution. A key benefit of software agents is their ability to automatically perform complex tasks in place of human operators. After briefly reviewing traditional fault diagnosis and software agent technologies, this paper discusses how these technologies can be used to support the drastic manning reduction requirements for future navy ships. Examples are given on the existing naval applications and research on detection, diagnostic, and prognostic software agents. Current work on a multiagent system for shipboard power systems is presented as an example of system-level application.
Keywords :
electric vehicles; fault diagnosis; multi-agent systems; naval engineering computing; ships; fault detection; fault diagnostics; fault prognostics; navy all-electric ships; predictive maintenance; software multiagents; Application software; Availability; Fault detection; Fault diagnosis; Humans; Marine vehicles; Multiagent systems; Predictive maintenance; Software agents; Software systems; Diagnostics; electric ship; fault detection; multiagent system (MAS); prognostics;
fLanguage :
English
Journal_Title :
Vehicular Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9545
Type :
jour
DOI :
10.1109/TVT.2007.897219
Filename :
4273732
Link To Document :
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