基于改进的VGG网络的钢丝绳内部损坏识别研究
1School of Mechanical, Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
本研究介绍了一种增强的VGG模型,用于使用时间频谱图检测内部电缆绳损伤. 改进的模型在识别电缆绳缺陷方面实现了高准确性和效率.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 检测电缆绳的内部损伤是一项挑战.
- 现有的方法可能缺乏准确性和效率.
研究的目的:
- 开发一个改进的VGG模型,用于准确的内部电缆绳损坏识别.
- 通过特定的修改来提高VGG模型的性能.
主要方法:
- 电线绳损坏信号用短时间里叶变换转化为时间频谱图.
- 通过注意力机制,批量规范化和优化聚合/完全连接层来增强VGG模型.
- 联合样本均分布的交叉作为损失函数.
主要成果:
- 改进的VGG模型在内部电缆绳损坏方面实现了高达98.84%的识别准确性.
- 该模型表明,计算成本降低,培训时间缩短.
- 与传统的VGG模型相比,增强了泛化能力.
结论:
- 提议的增强型VGG模型有效地识别了内部电缆绳损坏.
- 这些修改显著提高了识别准确性,效率和概括性.
- 这种方法为电缆绳完整性监测提供了强大的解决方案.
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