A survey on LED Prognostics and Health Management and uncertainty reduction

• DOI: 10.1016/j.microrel.2024.115399
• Publication Date: 2024-04-19T16:05:01Z
• Authors: Roberto Rocchetta; Elisa Perrone; Alexander Herzog; Pierre Dersin; Alessandro Di Buc...
• Keywords: Prognostics

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 tools online monitoring data collection. Uncertainty quantification (UQ) uncertainty reduction are essential to achieve assess the effect of heterogeneous operational-environmental conditions, lack data, noises on LED durability. Aleatory is considered in hybrid frameworks, probabilistic applied account inherent variability randomness lifetime. On other hand, often neglect epistemic uncertainty, lacking formal characterization methods. In this survey, we propose an overview accelerated collection methods modeling options LEDs. contrast works, review focuses fusion PHM optimal design experiment reduction. particular, optimizing a combination statistical optimality criteria degradation test schemes can substantially reduce enhance performance prognostic models.