Reliability testing and machine learning approach for modelling high-power light-emitting diode reliability

Reliability testing and machine learning approach for modelling high-power light-emitting diode reliability

Článek Scopus

The high-power Light Emitting Diode (LED) is a specialized type of LED that has found extensive use in a wide array of fields, particularly in areas such as lighting, signalling, and medical applications due to their cost-effectiveness and replace-ability. As a result of significant technological advancements, high-power LEDs have undergone rapid development, leading to improvements in quality, variety, and application. Within the realm of reliability research, high-power LEDs have garnered considerable attention. The primary aim of this paper is to conduct a comprehensive exploration and analysis of the existing methodologies for testing the reliability of high-power LEDs. This endeavour will involve a thorough investigation into the types of objects utilized for testing, the diverse testing methods employed, the techniques for data collection, and the parameters measured during testing. Furthermore, the paper aims to delve into the potential application of machine learning techniques for modelling, estimating, and predicting the reliability of high-power LEDs. The anticipated outcomes of this paper are intended to establish the foundation for the adoption of innovative approaches in reliability testing and to enhance the prediction and estimation of high-power LEDs reliability.

The high-power Light Emitting Diode (LED) is a specialized type of LED that has found extensive use in a wide array of fields, particularly in areas such as lighting, signalling, and medical applications due to their cost-effectiveness and replace-ability. As a result of significant technological advancements, high-power LEDs have undergone rapid development, leading to improvements in quality, variety, and application. Within the realm of reliability research, high-power LEDs have garnered considerable attention. The primary aim of this paper is to conduct a comprehensive exploration and analysis of the existing methodologies for testing the reliability of high-power LEDs. This endeavour will involve a thorough investigation into the types of objects utilized for testing, the diverse testing methods employed, the techniques for data collection, and the parameters measured during testing. Furthermore, the paper aims to delve into the potential application of machine learning techniques for modelling, estimating, and predicting the reliability of high-power LEDs. The anticipated outcomes of this paper are intended to establish the foundation for the adoption of innovative approaches in reliability testing and to enhance the prediction and estimation of high-power LEDs reliability.

data management; estimation method; innovation; machine learning; testing method

data management; estimation method; innovation; machine learning; testing method

LA, Q.; VINTR, Z.; ŽÁK, L.; VALIŠ, D.; KOHL, Z.

01.01.2025

Entomological Society of America

Annals of the Entomological Society of America

413

Spojené království Velké Británie a Severního Irska

333

338

6

https://doi.org/10.1051/matecconf/202541303005