Yang Wanyu, Wu Kunping, Long Bing, Tian Shulin
School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen 518000, China.
Sensors (Basel). 2024 Apr 29;24(9):2841. doi: 10.3390/s24092841.
The remaining useful life (RUL) prediction of RF circuits is an important tool for circuit reliability. Data-driven-based approaches do not require knowledge of the failure mechanism and reduce the dependence on knowledge of complex circuits, and thus can effectively realize RUL prediction. This manuscript proposes a novel RUL prediction method based on a gated recurrent unit-convolutional neural network (GRU-CNN). Firstly, the data are normalized to improve the efficiency of the algorithm; secondly, the degradation of the circuit is evaluated using the hybrid health score based on the Euclidean and Manhattan distances; then, the life cycle of the RF circuits is segmented based on the hybrid health scores; and finally, an RUL prediction is carried out for the circuits at each stage using the GRU-CNN model. The results show that the RMSE of the GRU-CNN model in the normal operation stage is only 3/5 of that of the GRU and CNN models, while the prediction uncertainty is minimized.
射频电路剩余使用寿命（RUL）预测是评估电路可靠性的重要手段。基于数据驱动的方法无需了解故障机制，减少了对复杂电路知识的依赖，从而能有效实现RUL预测。本文提出了一种基于门控循环单元-卷积神经网络（GRU-CNN）的新型RUL预测方法。首先，对数据进行归一化处理以提高算法效率；其次，基于欧氏距离和曼哈顿距离的混合健康评分评估电路的退化情况；然后，根据混合健康评分对射频电路的生命周期进行分段；最后，使用GRU-CNN模型对各阶段的电路进行RUL预测。结果表明，GRU-CNN模型在正常运行阶段的均方根误差（RMSE）仅为GRU和CNN模型的3/5，同时预测不确定性降至最低。
