Lumen Degradation Lifetime Prediction for High-Power White LEDs Based on the Gamma Process Model

• Main Authors: Mesfin Seid Ibrahim, Jiajie Fan, Winco K.C. Yung, Zeyu Wu, Bo Sun
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Photonics Journal
• Subjects: Light-emitting diodes (LEDs); luminous flux degradation; gamma distributed degradation (GDD); maximum likelihood estimation; method of moments.
• Online Access: https://ieeexplore.ieee.org/document/8887259/

Nowadays, due to the advancement of design and manufacturing technology, there are many consumer products with high reliability. Similarly, the competition in the business sector influences the product development time to become shorter and that makes it difficult for manufacturers to evaluate the reliability of current products before new products are released to the market. This phenomenon is manifested in the lighting industry, especially for the high power white light-emitting diodes (LEDs) as these products have a long lifetime and high reliability. Currently, the standard to predict the lifetime of LEDs is based on a deterministic nonlinear least squares method which has low prediction accuracy. To overcome this, degradation models are being used to study the reliability of such products, considering the uncertainties and the quality characteristics whose degradation over a period of time can be related to the product lifetime. A stochastic approach based on gamma distributed degradation (GDD) is proposed in this study to estimate the long-term lumen degradation lifetime of phosphor-converted white LEDs. An accelerated thermal degradation test was designed to gather luminous flux degradation data which was analyzed based on maximum likelihood estimation (MLE) and the method of moments (MM) to estimate the parameters for the GDD model. The MLE method has shown superiority over MM in terms of the estimation of the model parameters due to its iterative algorithm being likely to find the optimal estimation. The lifetime prediction results show that the accuracy of the proposed method is much better than the TM-21 nonlinear least squares (NLS) approach which makes it promising for future industrial applications.