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基于深度卷积神经网络的电缆绝缘材料的气体生产分类方法
Zihao Wang1, Yinan Chai1, Jingwen Gong1
1School of Electrical Engineering, Sichuan University, Wuhou District, Chengdu 610207, China.
Polymers
|January 28, 2026
概括
一个新的深度学习模型使用进化气体分析准确地识别了电源电缆绝缘中的多个故障模式. 这种先进的方法提高了关键电气设备的诊断准确度.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 进化气体分析 (EGA) 对于非侵入性地评估电源电缆绝缘状况至关重要.
- 目前的方法与同时老化机制以及在混合气体数据中识别多个故障模式作斗争.
研究的目的:
- 开发一种智能分析方法,用于准确评估绝缘状况.
- 提出基于深层卷积神经网络 (DCNN) 的多标签分类框架.
主要方法:
- 使用了来自五种绝缘材料 (EPDM,EVA,SR,PA,XLPE) 的六种特征性气体的度数据.
- 应用数据分析技术 (对数转换,Z分数规范化) 和DCNN与多尺度卷积,残余连接和注意力机制.
- 在降解状态的多标签分类中使用加权二进制交叉损失.
主要成果:
- 该DCNN模型有效地学习了特定材料的气体生成模式.
- 同时准确识别复杂的并发故障模式和多个退化状态.
- 在识别并发故障场景方面表现出卓越的性能.
结论:
- 拟议的DCNN框架提高了电源电缆绝缘状况评估的准确性和全面性.
- 提供了一种强大的智能方法,用于诊断关键电气设备中的复杂故障条件.
- 为提高电力电缆基础设施的可靠性提供技术支持.
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