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

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.

Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap

“…Apart from algorithmic innovations for general applications, a large proportion of research outcomes also focuses on the RUL prediction of specific components or devices, for instance, power transformers and transducers [71], [75], [79]; supercapacitors [78], [80]; transistors (MOSFET, IGBT) and diodes [62], [70], [81]; bearings [66], [67], etc. Other previous review can be found in [82], [83], [60], [84], [77] and the references therein.…”

Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes

“…Nevertheless, it is worth noting that the gamma process appears suitable for the monotonic degradation process, and this may Reproduced with permission. [125] Copyright 2019, IEEE.…”

“…Recently, the gamma process has been employed to model the lifetime of high-power white LEDs based on CCT shift. [124] Ibrahim et al [125] also used gamma process to model reliability of phosphor-converted white LEDs by estimating the long-term lumen maintenance lifetime and validate by comparing with the NLS regression method. The results showed that the prediction accuracy of the gamma process was superior compared with the NLS regression-based approach.…”

“…Gamma process (GP) [ 87,125] • Suitable for unidirectional degradation process • Mathematically, equations are easy to understand and underlying physical meaning is easy to comprehend.…”

Investigation on Circadian Action and Color Quality in Laser-Based Illuminant for General Lighting and Display

“…Solid-State lighting is one of the most promising lighting technologies in the 21st century, and has become the dominant technology for the general lighting [1]. The solid-state lighting technology based on light-emitting diodes (LEDs) and laser diodes (LDs) has gained widespread attention [2], [3]. In recent years, LEDs are replacing conventional incandescent lamps due to several well-known advantages, such as color-tunable property, long lifespan, compact size, and high luminous efficacy among others [4], [5].…”