A survey on LED Prognostics and Health Management and uncertainty reduction

Abstract

Hybrid Prognostics and Health Management (PHM) frameworks for light-emitting diodes (LEDs) seek accurate remaining useful life (RUL) predictions by merging information from physics-of-failure laws with data-driven models and tools for online monitoring and data collection. Uncertainty quantification (UQ) and uncertainty reduction are essential to achieve accurate predictions and assess the effect of heterogeneous operational-environmental conditions, lack of data, and noises on LED durability. Aleatory uncertainty is considered in hybrid frameworks, and probabilistic models and predictions are applied to account for inherent variability and randomness in the LED lifetime. On the other hand, hybrid frameworks often neglect epistemic uncertainty, lacking formal characterization and reduction methods. In this survey, we propose an overview of accelerated data collection methods and modeling options for LEDs. In contrast to other works, this review focuses on uncertainty quantification and the fusion of hybrid PHM models with optimal design of experiment methods for epistemic uncertainty reduction. In particular, optimizing the data collection with a combination of statistical optimality criteria and accelerated degradation test schemes can substantially reduce the epistemic uncertainty and enhance the performance of hybrid prognostic models.