TY - JOUR
T1 - Optimal burn-in policies for multiple dependent degradation processes
AU - Shi, Yue
AU - Xiang, Yisha
AU - Liao, Ying
AU - Zhu, Zhicheng
AU - Hong, Yili
N1 - Funding Information:
This work was supported in part by the U.S. National Science Foundation under Award 1855408.
Publisher Copyright:
© Copyright © 2020 “IISE”.
PY - 2020
Y1 - 2020
N2 - Many complex engineering devices experience multiple dependent degradation processes. For each degradation process, there may exist substantial unit-to-unit heterogeneity. In this article, we describe the dependence structure among multiple dependent degradation processes using copulas and model unit-level heterogeneity as random effects. A two-stage estimation method is developed for statistical inference of multiple dependent degradation processes with random effects. To reduce the heterogeneity, we propose two degradation-based burn-in models, one with a single screening point and the other with multiple screening points. At each screening point, a unit is scrapped if one or more degradation levels pass their respective burn-in thresholds. Efficient algorithms are devised to find optimal burn-in decisions. We illustrate the proposed models using experimental data from light-emitting diode lamps. Impacts of parameter uncertainties on optimal burn-in decisions are investigated. Our results show that ignoring multiple dependent degradation processes can cause inferior system performance, such as increased total costs. Moreover, a higher level of dependence among multiple degradation processes often leads to longer burn-in time and higher burn-in thresholds for the two burn-in models. For the multiple-screening-point model, a higher level of dependence can also result in fewer screening points. Our results also show that burn-in with multiple screening points can lead to potential cost savings.
AB - Many complex engineering devices experience multiple dependent degradation processes. For each degradation process, there may exist substantial unit-to-unit heterogeneity. In this article, we describe the dependence structure among multiple dependent degradation processes using copulas and model unit-level heterogeneity as random effects. A two-stage estimation method is developed for statistical inference of multiple dependent degradation processes with random effects. To reduce the heterogeneity, we propose two degradation-based burn-in models, one with a single screening point and the other with multiple screening points. At each screening point, a unit is scrapped if one or more degradation levels pass their respective burn-in thresholds. Efficient algorithms are devised to find optimal burn-in decisions. We illustrate the proposed models using experimental data from light-emitting diode lamps. Impacts of parameter uncertainties on optimal burn-in decisions are investigated. Our results show that ignoring multiple dependent degradation processes can cause inferior system performance, such as increased total costs. Moreover, a higher level of dependence among multiple degradation processes often leads to longer burn-in time and higher burn-in thresholds for the two burn-in models. For the multiple-screening-point model, a higher level of dependence can also result in fewer screening points. Our results also show that burn-in with multiple screening points can lead to potential cost savings.
KW - Degradation-based burn-in
KW - copulas
KW - multiple dependent degradation processes
KW - multiple screening points
KW - two-stage estimation method
UR - http://www.scopus.com/inward/record.url?scp=85097377008&partnerID=8YFLogxK
U2 - 10.1080/24725854.2020.1841344
DO - 10.1080/24725854.2020.1841344
M3 - Article
AN - SCOPUS:85097377008
VL - 53
SP - 1281
EP - 1293
JO - IISE Transactions
JF - IISE Transactions
SN - 2472-5854
IS - 11
ER -