TY - JOUR
T1 - A new generalized burn-in procedure for items in stochastically evolving population
AU - Lee, Hyunju
AU - Cha, Ji Hwan
N1 - Funding Information:
The authors would like to thank the Editor and referees for careful reading and valuable comments. This work was supported by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (no. 2009-0093827 ).
Publisher Copyright:
© 2016 Elsevier Inc.
PY - 2016/10/1
Y1 - 2016/10/1
N2 - In practice, the reliability performance of items in operation strongly depends on the operational history or the working environment. Thus, most often, the population stochastically evolves depending on it rather than being static. For example, the failure and repair history strongly affects the future reliability performance of the item in operation. In this case, even though the population of items initially starts from a homogeneous one, it gradually evolves to a heterogeneous one. Until now, most studies on burn-in have been performed assuming that the population is static. In this paper, a new generalized burn-in procedure for items in stochastically evolving population is developed and its stochastic properties are analyzed. Based on the obtained theoretical results, the optimal burn-in procedure which balances two criteria is studied.
AB - In practice, the reliability performance of items in operation strongly depends on the operational history or the working environment. Thus, most often, the population stochastically evolves depending on it rather than being static. For example, the failure and repair history strongly affects the future reliability performance of the item in operation. In this case, even though the population of items initially starts from a homogeneous one, it gradually evolves to a heterogeneous one. Until now, most studies on burn-in have been performed assuming that the population is static. In this paper, a new generalized burn-in procedure for items in stochastically evolving population is developed and its stochastic properties are analyzed. Based on the obtained theoretical results, the optimal burn-in procedure which balances two criteria is studied.
KW - Dependent increment property
KW - Operational history
KW - Optimal burn-in
KW - Stochastic intensity
KW - Stochastically evolving population
UR - http://www.scopus.com/inward/record.url?scp=84975519759&partnerID=8YFLogxK
U2 - 10.1016/j.apm.2016.04.025
DO - 10.1016/j.apm.2016.04.025
M3 - Article
AN - SCOPUS:84975519759
SN - 0307-904X
VL - 40
SP - 8338
EP - 8351
JO - Applied Mathematical Modelling
JF - Applied Mathematical Modelling
IS - 19-20
ER -