Incorporating data warehouse technology into asset information management systems for large assets

Amy J.C. Trappey*, Charles Trappey, Lin Ma, Acer C.C. Chang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

Large sized engineering assets such as power transformers are critical parts of the power supply networks. Therefore, focusing on early fault diagnosis to maintain large transformers in good condition is an important operational task to the power companies. In order to manage and fully utilize the big data generated from the large number of transformers, this paper incorporates data warehouse technology to a fault diagnosis system for the entire transformer fleet. The research includes two major parts. First, a data warehouse (DW) is designed for the large assets information management. Then, the DW-based intelligent fault diagnosis system is developed and implemented. The DW stores the complete transformers’ data and then different data cubes are defined according to various applications. The fault diagnosis system for power transformers consists of the condition monitoring module, failure diagnosis module, and intelligent decision supports module. The research methodology and prototype system are verified with real data from a series of 161 kV transformers in operations.

Original languageEnglish
Title of host publicationLecture Notes in Mechanical Engineering
PublisherPleiades Publishing
Pages601-612
Number of pages12
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Mechanical Engineering
VolumePartF4
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Keywords

  • Data mining
  • Data warehouse
  • Engineering asset management
  • Fault diagnosis
  • Transformer

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