Moon-Hwan Chang1, Myeongsu Kang2, and Michael Pecht2
1 Samsung Display Co., Ltd., Yongin-si 17113, South Korea 2 Center for Advanced Life Cycle Engineering, University ofMaryland, College Park,MD 20742 USA
Light-emitting diodes (LEDs) are widely used
for general lighting and display applications. As the demand
for LEDs has grown, the need to quickly qualify them
has emerged. To address this issue, this paper introduces a
prognostics-based qualification method using an efficient
relevance vector machine (RVM) regression model that
reduces the qualification testing time of LEDs from 6000 h
(as recommended by industry standards) to 210 h. The
developed method predicts LED remaining useful life (RUL)
by calculating the accumulated sum of products of similarity
weights and historical LED RUL values at the 210th hour.
Specifically, a similarity weight, defined as the degree of
affinity between two different LED’s degradation trends, is
derived from the difference between a test unit’s degradation
trend and a training unit’s degradation trend. Likewise,
the RVM is used to represent a unit’s degradation behavior
and facilitates the reduction of unit-to-unit variations by
precisely capturing transient degradation dynamics.
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