Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494084
Title: Intelligent information retrieval and fault diagnosis for the asset management of power substations
Author: Yang, Zhen
ISNI:       0000 0001 2420 3230
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2008
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Abstract:
This thesis mainly presents two intelligent approaches to the Asset Management (AM) of power substations, which include an Evidential Reasoning (ER)-based document ranking approach to an Ontology-based Document Search Engine (ODSE) for the Information Retrieval (IR) of power substations and an Association Rule Mining (ARM)-based Dissolved Gas Analysis (DGA) approach to the Fault Diagnosis (FD) of power transformers.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.494084  DOI: Not available
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