机器学习通过光谱功率分布建模辅助led的早期异常检测

Minne Liu, M. Ibrahim, Minzhen Wen, Sheng Li, An Wang, Guoqi Zhang, Jiajie Fan
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引用次数: 0

摘要

光谱功率分布(SPD)是不同波长下的辐射功率强度,包含了光源最基本的光度和比色性能,能够预测led的寿命。本文提出了一种利用机器学习算法辅助的SPD模型来检测白光led的早期故障。首先利用高斯、洛伦兹和Asym2sig函数的统计模型提取3W大功率白光led的SPD特征。然后使用无监督学习方法主成分分析(PCA)对提取的特征参数进行降维。接下来,采用基于k近邻(KNN)的方法,将主簇划分为若干组,并估计每个簇的质心到测试点的距离,从而检测led的异常。结果表明:(1)对于选定的白光led, Asym2sig函数的拟合效果优于高斯函数和洛伦兹函数;(2)机器学习方法可以显著辅助LED异常检测,将异常检测时间减少到789.6 h,而IES TM21要求的流明维持退化达到70%时需要1311 h。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning assisted early anomaly detection of LEDs with spectral power distribution modeling
Spectral power distribution (SPD) is the radiation power intensity at different wavelengths, containing the most basic photometric and colorimetric performance of the illuminant, which is able to predict the lifetime of LEDs. This paper proposes an SPD model assisted by machine learning algorithms to detect the early failure of white LEDs. The SPD features of 3W high-power white LEDs were firstly extracted by the statistical models of Gaussian, Lorentz, and Asym2sig functions. An unsupervised learning method, principal component analysis (PCA), was then used to reduce the extracted features parameters’ dimensions. Next a K-nearest neighbor (KNN)-based method was used to detect LEDs’ anomalies by dividing the main cluster into groups, and estimating the distance from the center of mass of each cluster to the test point. The results showed the following: (1) for selected white LEDs, the Asym2sig function has a better fitting result than Gaussian and Lorentz functions; (2) machine learning methods can significantly assist in LED anomaly detection and can decrease the amount of anomaly detection time to 789.6 h, compared to the 1311 h when lumen maintenance degradation reaches 70% as required by IES TM21.
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