PHM of Light‐Emitting Diodes

This chapter provides an overview of the prognostics and health management (PHM) methods applied to light‐emitting diodes (LEDs). These methods include optimizing LED design with simulations, shortening qualification test times, enabling condition‐based maintenance (CBM) for LED systems, and providing information for return on investment (ROI) analysis. The simulation has unique characteristics for LED lighting systems, such as parameter selections and assumptions. The chapter describes the latest information regarding the prognostics of high‐power white LEDs and physical modeling and failure analysis for LEDs. It also provides an overview of the available prognostic methods and models that have been applied to both LED devices and LED systems. These methods include statistical regression, static Bayesian network (BN), Kalman filtering (KF), particle filtering (PF), artificial neural network (ANN), and physics‐based methods. Degradation processes vary one‐directionally and are monotonic, for example, light output degradation processes of LEDs.