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Predicting failure of remote battery backup systems

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Date Issued:
2013
Summary:
Uninterruptable Power Supply (UPS) systems have become essential to modern industries that require continuous power supply to manage critical operations. Since a failure of a single battery will affect the entire backup system, UPS systems providers must replace any battery before it runs dead. In this regard, automated monitoring tools are required to determine when a battery needs replacement. Nowadays, a primitive method for monitoring the battery backup system is being used for this task. This thesis presents a classification model that uses data mining cleansing and processing techniques to remove useless information from the data obtained from the sensors installed in the batteries in order to improve the quality of the data and determine at a given moment in time if a battery should be replaced or not. This prediction model will help UPS systems providers increase the efficiency of battery monitoring procedures.
Title: Predicting failure of remote battery backup systems.
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Name(s): Aranguren, Pachano Liz Jeannette, author
Khoshgoftaar, Taghi M., Thesis advisor
College of Engineering and Computer Science, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: single unit
Date Created: Fall 2013
Date Issued: 2013
Publisher: Florida Atlantic University
Physical Form: Online Resource
Extent: 73 p.
Language(s): English
Summary: Uninterruptable Power Supply (UPS) systems have become essential to modern industries that require continuous power supply to manage critical operations. Since a failure of a single battery will affect the entire backup system, UPS systems providers must replace any battery before it runs dead. In this regard, automated monitoring tools are required to determine when a battery needs replacement. Nowadays, a primitive method for monitoring the battery backup system is being used for this task. This thesis presents a classification model that uses data mining cleansing and processing techniques to remove useless information from the data obtained from the sensors installed in the batteries in order to improve the quality of the data and determine at a given moment in time if a battery should be replaced or not. This prediction model will help UPS systems providers increase the efficiency of battery monitoring procedures.
Identifier: FA0004002 (IID)
Note(s): Includes bibliography.
Thesis (M.S.)--Florida Atlantic University, 2013.
Subject(s): Electric power systems -- Equipment and supplies
Energy storing -- Testing
Lead acid batteries
Power electronics
Protective relays
Held by: Florida Atlantic University Digital Library
Sublocation: Boca Raton, Fla.
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA0004002
Restrictions on Access: All rights reserved by the source institution
Restrictions on Access: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU