Machine learning techniques for diagnosing and locating faults through the automated monitoring of power electronic components in shipboard power systems

IEEE Electric Ship Technologies Symposium, ESTS 2009

Volumn , Issue , 2009, Pages 469-476

  Mair, A J ^a   Davidson, E M ^a   McArthur, S D J ^a   Srivastava, S K ^b   Schoder, K ^b   Cartes, D A ^b  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^b FLORIDA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED MONITORING; COMPARATIVE TESTING; DC SYSTEM; FAULT CONDITIONS; LOCAL MEASUREMENT; MACHINE LEARNING TECHNIQUES; MEDIUM VOLTAGE; NEAREST-NEIGHBOUR CLASSIFIER; POWER DISTRIBUTION ARCHITECTURE; POWER ELECTRONIC COMPONENTS; POWER SYSTEM ARCHITECTURES; SHIPBOARD POWER SYSTEM;

BAYESIAN NETWORKS; DECISION TREES; ELECTRIC POTENTIAL; ELECTRIC POWER TRANSMISSION NETWORKS; ELECTRIC SHIP EQUIPMENT; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING ALGORITHMS; MACHINE COMPONENTS; OPTICAL TOMOGRAPHY; POWER TRANSMISSION; ROBOT LEARNING; SHIPS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 67650266707     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ESTS.2009.4906553     Document Type: Conference Paper

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