TY - GEN
T1 - On predicting the times to failure of power equipment
AU - Begovic, Miroslav
AU - Djuric, Petar
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/77951719028
U2 - 10.1109/HICSS.2010.290
DO - 10.1109/HICSS.2010.290
M3 - Conference contribution
AN - SCOPUS:77951719028
SN - 9780769538693
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
BT - Proceedings of the 43rd Annual Hawaii International Conference on System Sciences, HICSS-43
T2 - 43rd Annual Hawaii International Conference on System Sciences, HICSS-43
Y2 - 5 January 2010 through 8 January 2010
ER -