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基于注意力机制的多分支残留连接频谱图像分类研究
Zhong Xiaohui1, Dong Sheng1, Zhang Yiyi1
1Ningbo Beilun Third Container Terminal Co., LTD, Ningbo, 315000, Zhejiang, China.
Scientific reports
|July 15, 2025
概括
这项研究引入了一个高效的全球注意力 (EGA) 网络用于声谱图像分类,改进降噪和部分放电检测在门架起重机.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 卷积神经网络 (CNN) 用于光谱图像分类.
- 声谱图中的噪音数据可能会阻碍特征提取.
- 部分放电检测对于设备维护至关重要.
研究的目的:
- 提出一个新的多分支,剩余连接的高效全球关注 (EGA) 网络.
- 通过减少噪声来提高声谱图像分类的准确性.
- 为了提高检测异常的部分放电类型在门架起重机.
主要方法:
- 开发了一个多个分支机构的残余连接的EGA网络,结合了改进的全球关注机制 (IGAM).
- 通过将声学特征与光谱噪声分开来实现降噪.
- 从噪声降低信号中提取的相位解析部分放电 (PRPD) 频谱.
- 利用剩余连接和面具分支用于特征融合.
主要成果:
- 拟议的EGA网络有效地减少了光谱噪声.
- 在碳刷中实现了更准确的异常部分放电检测.
- 通过专注于道和空间特征,证明了改进的特征提取能力.
结论:
- 新的EGA网络为声谱图像分类提供了可行和创新的解决方案.
- 该方法显著提高了部分放电检测的准确性.
- 实验和生产使用验证了拟议方法的有效性.
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