On Predicting the Times to Failure of Power Equipment Conference Paper

abstract

• Across power systems, large classes of identical devices can be found which support the system operation (transformers, breakers, switches, utility poles, etc.) The problem of their operational management is often aggravated by in-service failures and associated additional costs. Part of asset management strategy is to learn the failure characteristics of classes of devices in service and attempt to formulate the preventive replacement strategy based on that information. The paper presents an algorithm based on Bayesian learning which enables predictions of times to failure of identical devices to be refined with accumulated experience. 2010 IEEE.

name of conference

• 2010 43rd Hawaii International Conference on System Sciences

authors

• Begovic, Miroslav

published proceedings

• 2010 43rd Hawaii International Conference on System Sciences

author list (cited authors)

• Begovic, M., & Djuric, P.

citation count

• 0

complete list of authors

• Begovic, Miroslav||Djuric, Petar

publication date

• January 2010

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences  Journal

keywords

• Asset Management
• Bayes Methods
• Bayesian Learning
• Bayesian Methods
• Cables
• Costs
• Failure Analysis
• In-service Failures
• Operational Management
• Paramagnetic Resonance
• Power Apparatus
• Power Equipment Failure
• Power Industry
• Power System Management
• Switches
• System Operation
• Telephone Poles
• Time Prediction
• Transformers

Digital Object Identifier (DOI)

• 10.1109/hicss.2010.290

International Standard Book Number (ISBN) 13

• 9780769538693

start page

• 1

end page

• 6

URL

• http://dx.doi.org/10.1109/hicss.2010.290