Abstract
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.
Highlights
In the past few years, it has been challenging to evaluate the reliability of highly reliable products such as light-emitting diodes (LEDs), aircraft components, lithium-ion batteries, etc., based on classical approaches including censoring and/or accelerated lifetime-based methods that record time-to-failure
The lifetime prediction of phosphor converted white LEDs is demonstrated based on a stationary gamma process approach that considers the degradation at constantly increasing operating times
As one of the dominant quality characteristics of white LEDs, lumen maintenance prediction is based on the nonlinear least squares method which introduced prediction inaccuracy
Summary
In the past few years, it has been challenging to evaluate the reliability of highly reliable products such as light-emitting diodes (LEDs), aircraft components, lithium-ion batteries, etc., based on classical approaches including censoring and/or accelerated lifetime-based methods that record time-to-failure. This is due to the long lifetime of the products that contributes to the recording of few or no failures during the reliability testing and little information can be extracted from such life data [1], [2]. The non-linear as well as random and dynamic variation multiple performance characteristics of products can be explained with the implantation of stochastic processes [14]
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