一个有针对性的一维全卷积自动编码器网络,用于对磁流泄漏数据的智能压缩
Wenbo Xuan1, Pengchao Chen2, Rui Li2
1PipeChina Institute of Science and Technology, No. 4 Dongting 1st Street, Tianjin Economic-Technological Development Area, Tianjin, 300457, China. xuanwb@pipechina.com.cn.
Scientific reports
|April 3, 2025
概括
本研究引入了用于磁流泄漏 (MFL) 测试的智能数据压缩方法,显著改善了压缩比,并减少了管道检查数据的重建错误.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 磁流泄漏 (MFL) 非破坏性测试的大量数据在石油和天然气管道检查中带来了挑战.
- 有效的数据压缩对于有效管理和分析MFL数据至关重要.
研究的目的:
- 为MFL测试数据提出智能数据压缩方法.
- 为了减少数据量,同时保持管道完整性评估的数据完整性.
主要方法:
- 开发了一个有针对性的一维全卷积自编码器 (1D-FCAE) 网络.
- 实施数据预处理和数据块分类算法来对MFL数据进行分类.
- 为了实现压缩和重建,针对不同的MFL数据类型构建了不同的1D-FCAE模型.
主要成果:
- 与主要组件分析 (PCA) 相比,提出的方法实现了平均绝对误差 (MAE) 的27.7%降低和压缩比率的14%改善.
- 与ID-AE相比,1D-FCAE模型显示了参数,内存使用和训练时间的显著减少.
- 压缩和解压缩时间显著减少,这表明其实际适用性.
结论:
- 有针对性的1D-FCAE方法为MFL数据压缩提供了有效的解决方案.
- 该方法适用于资源有限的工业环境,提高管道非破坏性测试的效率.
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