基于BiTCN-注意力转移学习模型的棒控制电源柜中电源设备的故障预测研究
Zhi Chen1,2, Liqi Ye1,2, Yifan Jian1
1National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu 610213, China.
Micromachines
|November 27, 2024
概括
这项研究引入了一种新的转移学习模型,用于预测核电站在不同操作条件下的绝缘门双极晶体管 (IGBT) 故障. 拟议的模型达到99%以上的准确性,提高了可靠性和维护性.
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 机器学习
背景情况:
- 绝缘门双极晶体管 (IGBT) 对核电站的安全性和效率至关重要.
- 现有的IGBT故障预测方法由于不同的操作条件而难以准确.
- 交叉条件故障预测对于提高核电站控制系统的可靠性至关重要.
研究的目的:
- 为核电站控制柜中的IGBT开发一个准确的交叉条件故障预测方法.
- 利用多个来源的工作条件数据来提高故障预测可靠性.
- 解决单一条件故障预测模型的局限性.
主要方法:
- 开发了一种新的转移学习 (TL) 模型,将双向时间卷积网络 (BiTCN) 与注意力结合起来.
- 一个IGBT故障模拟模型在各种工作条件下生成了生命周期数据.
- 核心主要组件分析 (KPCA) 在模型训练和微调之前集成和预处理数据.
主要成果:
- 拟议的BiTCN注意力TL模型实现了超过99%的预测准确性.
- 这种准确性明显优于传统的转移学习模型,如RNN-TL,LSTM-TL,GRU-TL和TCN-TL.
- 该模型有效地减少了因工作条件变化引起的特征价值不一致.
结论:
- 双TCN注意力TL模型为在多源域中交叉条件IGBT故障预测提供了有效的解决方案.
- 它能够准确地预测退化趋势,即使早期故障数据有限.
- 这种方法提高了核电站控制系统的可靠性和可维护性.
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