Light Emitting Diodes (LEDs) are among the most widely used electronic devices for everyday lighting applications due to their durability over incandescent lamps. However, there are no standardized approaches to predict the reliability of LEDs as they are manufactured and tested as per the user requirements and applications. This dramatically limits developing generic prognostic algorithms pertaining to predicting the remaining useful life (RUL) of LEDs. In this study, we propose a Bayesian optimized neural network approach to predict the lumen degradation trends of LEDs. The proposed method does not require an accurate physical model representing the LED degradation behavior and does not require a large amount of degradation data. We have used a particle filter algorithm to train a simple two-layer feedforward neural network model and use the trained model to predict the lumen degradation of LEDs. Also, the weight decay issues commonly encountered in particle filter algorithm are addressed using three different resampling strategies and particle roughening method. To evaluate the effectiveness of the proposed approach, Root Mean Squared Error (RMSE) and Relative Accuracy (RA) were used as the prognostic metrics.

发光二极管 (LED) 是日常照明应用中使用最广泛的电子设备之一，因为它们比白炽灯更耐用。但是，在根据用户要求和应用进行制造和测试时，没有标准化的方法来预测 LED 的可靠性。这极大地限制了开发与预测 LED 剩余使用寿命 (RUL) 有关的通用预测算法。在这项研究中，我们提出了一种贝叶斯优化神经网络方法来预测 LED 的流明退化趋势。所提出的方法不需要表示 LED 退化行为的准确物理模型，也不需要大量退化数据。我们使用粒子滤波算法训练了一个简单的两层前馈神经网络模型，并使用训练后的模型来预测 LED 的流明衰减。此外，使用三种不同的重采样策略和粒子粗糙化方法解决了粒子滤波算法中常见的权重衰减问题。为了评估所提出方法的有效性，均方根误差 (RMSE) 和相对准确度 (RA) 被用作预后指标。