Predicting Lumen Degradation of Light Emitting Diodes using Hybrid Particle Filter Trained Neural Networks

Abstract

High power white light emitting diodes (LEDs) are the widely opted eco-friendly alternate light source over incandescent lamps due to their lower power consumption and longer lifetime. The emerging market demand for LEDs brings about the critical need for adequate reliability testing and lifetime prognosis as they are predominantly used in uncontrolled environments. The common wear-out failure modes of LEDs include light output (luminosity) degradation and color shift. Commercially available LEDs have prolonged lifetime of about 70,000h which makes it impractical to obtain real-time degradation data, which subsequently complicates the ability to accurately predict the lumen maintenance life. Several attempts have been made by researchers to develop physics of failure models and / or data driven models for predicting the remaining useful life (RUL) of LEDs. However, these methods lack generalizability and do not address the prediction uncertainties caused by unit-to-unit variation. This calls for the need to develop robust prediction algorithms capable of handling variations in degradation trends due to manufacturing process, system design and environmental / operating conditions. This study proposes a hybrid prognostic approach which combines particle filters (PF) and neural networks (NN). The run-to-failure trend of one LED in a lot is used to model a feedforward neural network and the model parameters are optimized using the particle filter algorithm. The PF trained NN model is further used for RUL prediction for other LED devices in the lot enabling variations to be accounted for and naturally embedded into the prognostic framework. The accuracy of proposed hybrid approach was evaluated using RMSE as the performance metric.