Prognostics-Based LED Qualification Using Similarity-Based Statistical Measure With RVM Regression Model

Abstract

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.