用BO-CEEMDAN对IGBT进行基于执行者-KAN的故障预测
1School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430070, China.
Micromachines
|June 27, 2025
概括
预测绝缘门双极晶体管 (IGBT) 的故障对于系统可靠性至关重要. 一个新的Performer-KAN模型,使用贝叶斯优化的CEEMDAN进行特征提取,在IGBT故障预测中实现了卓越的准确性,计算成本低.
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
- 人工智能的人工智能
背景情况:
- 绝缘门双极晶体管 (IGBT) 在功率电子中至关重要,但容易发生故障.
- 预测IGBT故障对于确保系统可靠性和防止灾难性事件至关重要.
研究的目的:
- 为IGBT故障预测开发一个准确和高效的预后模型.
- 为工业应用增强实时健康监测和故障预测能力.
主要方法:
- 使用贝叶斯优化的CEEMDAN (BO-CEEMDAN) 来有效地从IGBT数据中提取故障特征.
- 提出了一种新的预测模型,即Performer-KAN,将Performer的FAVOR+机制与Kolmogorov-Arnold网络 (KAN) 结合起来,以提高非线性近似度.
主要成果:
- 表演者-KAN模型展示了卓越的预测准确性,通过MAE,RMSE和R2.2等指标进行验证.
- 与六个代表性深度学习模型相比,实现了更高的预测精度.
- 保持较低的计算开销,表明实际可用性.
结论:
- 拟议的Performer-KAN模型为实时IGBT健康监测提供了实用和有效的解决方案.
- 整合BO-CEEMDAN和Performer-KAN显著提高了IGBT故障预测的准确性和可靠性.
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