In-Situ early anomaly detection and remaining useful lifetime prediction for high-power white LEDs with distance and entropy-based long short-term memory recurrent neural networks

Wen, Minzhen; Ibrahim, Mesfin Seid; Meda, Abdulmelik Husen; Zhang, Guoqi; Fan, Jiajie

doi:10.1016/j.eswa.2023.121832

Cited by 4 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In traditional LEDs, there has been an evolution of advanced predictive strategies for estimating the remaining useful lifetime of LEDs, including the nonlinear least squares method, long short-term memory recurrent neural networks, convolutional neural networks, and bayesian methods. [8][9][10][11][12][13][14] In organic LEDs (OLEDs) and quantum-dot LEDs (QLEDs) cases, the degradation trend can be well fitted by using exponential decay models. [15][16][17][18] So, one usually only experimentally tests T 95 of their lifetimes, and the rest lifetimes can be predicted by the exponential decay models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Operational Lifetime of Perovskite Light Emitting Diodes by Machine Learning

Zhang,

Lu,

Tao

et al. 2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

Perovskite light‐emitting diodes (LEDs) with advantages of high electroluminescence efficiency at high brightness, good color purity, and tunable bandgap, are believed to have potential applications in the next generation display and lighting technologies. Due to the complex degradation process, mathematic models to describe the degradation process of perovskite LEDs are absent. In this work, it is found that the mathematical fitting methods which have been widely used to describe the decay trend of organic LEDs and quantum‐dot LEDs, are unable to accurately predict the lifetime of perovskite LEDs. Then an ensemble machine learning model is developed, which utilizes data augmentation technique to predict T50 of perovskite LEDs based on features before T80, achieving an accuracy of 0.995. Furthermore, the model can also accurately predict the T90 lifetime of quantum‐dot LEDs (QLEDs) using features before T98, suggesting it is a useful tool to efficiently evaluate LED lifetimes.

show abstract