性能降解建模及其IGBT门氧化物层的预测算法基于CNN-LSTM网络
Xin Wang1,2,3, Zhenwei Zhou2,3, Shilie He2,3
1School of Automation and Engineering, South China University of Technology, Guangzhou 510641, China.
Micromachines
|May 27, 2023
概括
这项研究建议使用门泄漏电流来预测绝缘门双极晶体管 (IGBT) 的降解. 一个CNN-LSTM模型准确地预测了门氧化物层的健康状况,实现了较低的预测误差.
科学领域:
- 电力电子 电力电子 电力电子
- 可靠性工程可靠性工程
- 机器学习用于诊断.
背景情况:
- 绝缘门双极晶体管 (IGBT) 在功率电子中至关重要,但它们的门氧化物层降解带来了显著的故障风险.
- 有效的健康管理需要准确预测IGBT性能退化.
- 现有的方法在预测门氧化物层故障方面缺乏足够的准确性.
研究的目的:
- 为了确定IGBT门氧化物层降解的可靠前体参数.
- 开发一个准确的健康指标来表征IGBT门氧化物降解.
- 为IGBTs构建和验证一个强大的降解预测模型.
主要方法:
- 门泄漏电流被选为门氧化物降解的前体参数.
- 使用特征选择和融合技术,包括时间域分析,灰色相关性,Mahalanobis距离和卡尔曼波器.
- 一个卷积神经网络和长期短期记忆 (CNN-LSTM) 网络被用于降解预测.
主要成果:
- 成功提取了一个健康指标,有效地描述了IGBT门氧化物降解的特征.
- 与LSTM,CNN,SVR和GPR模型相比,CNN-LSTM模型显示出更高的适配精度.
- 开发的模型在NASA-Ames数据集上的性能退化预测中实现了0.0216的低平均绝对误差.
结论:
- 门泄漏电流是预测IGBT门氧化物层故障的可行前体参数.
- 该CNN-LSTM模型为IGBT降解预测提供了高精度和可靠性.
- 拟议的方法提高了电力电子设备的健康管理.
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