Comparing prediction methods for LED failure measured with Transient Thermal Analysis

Zippelius, Andreas; Hans, Alexander; Liu, E; Schmid, Maximilian; Pérez-Velázquez, Judith; Elger, Gordon

doi:10.1109/eurosime48426.2020.9152657

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…This study underscores the benefits of the gated neural network, including easy convergence, high prediction accuracy, flexible algorithm integration, and substantial practical value, in comparison to the outcomes of artificial neural networks, RNNs, and LSTM. By contrasting the statistical approach with the artificial neural network, Zippelius et al (2020) successfully forecasted defects using transient thermal analysis (TTA) data of LED solder connections in a temperature shock test. The experimental findings highlight the value and enhanced accuracy of LSTM-ANN.…”

Section: Single Neural Networkmentioning

confidence: 99%

Overview of high-power LED life prediction algorithms

Sun,

Bai,

Zhang

2024

Front. Sustain. Energy Policy

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Life prediction significantly influences the reliability of LED light sources. While high-power LED light sources theoretically offer a lifespan of up to 100,000 h, irreversible damage to components leads to light failure, substantially reducing their actual lifespan. Consequently, accurate life prediction is pivotal for manufacturers to cut costs and enhance economic efficiency. This necessity aligns with the interests of communities, governments, and consumers. Currently, the most extensively employed prediction methods are based on traditional physical models and data-driven approaches. The focal point of current research lies in realizing model fusion, presenting both a hotspot and a challenge. To elucidate the relationships, advantages, and disadvantages of different algorithms and establish the groundwork for LED life prediction algorithm development, this paper first introduces material properties and the light decay model of high-power LED light sources. Subsequently, it discusses the principles and methods of the physical model concerning light source reliability. The paper also presents a review and comparison of recent domestic and foreign light source life prediction models. Finally, it provides insights into the expected future development trends in life prediction.

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Section: Single Neural Networkmentioning

confidence: 99%