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Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests

Authors
Park, Jong InBae, Suk Joo
Issue Date
Mar-2010
Publisher
Institute of Electrical and Electronics Engineers
Keywords
Accelerated degradation test; delta approximation; lifetime distribution-based procedure; multiple imputation; nonlinear random-coefficients model; organic light-emitting diode
Citation
IEEE Transactions on Reliability, v.59, no.1, pp 74 - 90
Pages
17
Indexed
SCIE
SCOPUS
Journal Title
IEEE Transactions on Reliability
Volume
59
Number
1
Start Page
74
End Page
90
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/175355
DOI
10.1109/TR.2010.2040761
ISSN
0018-9529
1558-1721
Abstract
Accelerated degradation testing (ADT) expedites product degradation by stressing the product beyond its normal use. To extrapolate the product's reliability at use condition, the ADT requires a known functional link relating the harsh testing environment to the usual use environment. Practitioners are often faced with a great challenge to designate an explicit form of the stress-degradation relationship a priori in accelerated degradation models. In this paper, we propose three methods to make direct inference on the lifetime distribution itself without invoking arbitrary assumptions on the degradation model: delta approximation, multiple imputation of failure-times, and the lifetime distribution-based (LDB) method. The methods are easy to implement without computational difficulty, hence they have potential in a wide range of applications for estimating lifetime distributions from ADT data. We applied the methods to two ADT data sets including a real application of commercial organic light-emitting diodes (OLED). The analysis of the examples and simulation results suggests parametric LDB and multiple imputation method as more potential alternatives to traditional failure-time approaches, especially for the case where there is neither enough physical background, nor historical evidence supporting presumed relationships between stress and the parameters of the degradation model.
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