一种新的方法来预测RF电路的剩余有用寿命,该方法基于式反复单元-卷积神经网络模型
Wanyu Yang1,2, Kunping Wu1, Bing Long1
1School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
本研究引入了一种新的封闭循环单元卷积神经网络 (GRU-CNN),用于预测射频电路的剩余使用寿命 (RUL),提高准确性和减少不确定性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 可靠性工程可靠性工程
背景情况:
- 预测剩余使用寿命 (RUL) 对射频电路可靠性至关重要.
- 数据驱动的方法为传统方法提供了有效的替代方案,绕过了复杂的故障机制知识.
研究的目的:
- 为RF电路提出一种新的RUL预测方法,使用一个封闭的反复单元卷积神经网络 (GRU-CNN).
- 为了提高RF电路的RUL预测的准确性和减少不确定性.
主要方法:
- 数据规范化以提高算法效率.
- 混合健康分数计算使用欧几里德和曼哈顿距离来评估电路退化.
- 基于混合健康评分的射频电路生命周期细分.
- 在每个生命周期阶段使用GRU-CNN模型进行RUL预测.
主要成果:
- 拟议的GRU-CNN模型在正常运行阶段实现了根平均平方误差 (RMSE),该误差是独立GRU和CNN模型的三分之五.
- 格鲁-CNN模型显著降低了预测不确定性.
结论:
- 与传统的GRU和CNN模型相比,GRU-CNN模型在RF电路的RUL预测中表现出卓越的性能.
- 拟议的方法通过准确的RUL预测有效地提高了RF电路的可靠性.
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