使用新型深度学习模型进行管道监控数据恢复:工程案例研究
Yong Zhao1, Xinpeng Zhang2, Yanli Liu2
1Xinjiang Yaxin CBM Exploration and Development Co., Ltd., Urumqi, China.
Frontiers in artificial intelligence
|October 23, 2025
概括
这项研究引入了一个新的PDO-BiGRU-GAN模型来恢复丢失的管道数据,显著改善结构健康评估. 该模型展示了智能管道监控的高精度和效率.
科学领域:
- 土木工程 土木工程是指土木工程.
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 管道监测中缺少的数据会影响结构健康评估.
- 不完整的数据阻碍了对管道完整性的全面评估.
研究的目的:
- 提出一种新的PDO-BiGRU-GAN模型,以有效地恢复管道监控中缺失的数据.
- 通过先进的数据完成技术,提高管道结构健康评估的准确性和可靠性.
主要方法:
- 开发了一个混合模型,集成草原狗优化 (PDO),双向门式循环单元 (BiGRU) 和生成对抗网络 (GAN).
- 使用基于现场的管道监测数据库进行模型培训和验证.
- 进行过超参数灵敏度分析和废弃性研究,以评估模块贡献.
主要成果:
- 在各种缺失数据场景中,PDO-BiGRU-GAN模型实现了高回收精度 (R2 > 0.93).
- 性能是最佳的,缺失数据比率低于20/24.
- 在准确性和错误指标 (MSE,RMSE,MAPE,MAE) 方面表现优于现有的八种深度学习模型.
结论:
- PDO-BiGRU-GAN模型为管道监控数据恢复提供了一个强大而可扩展的解决方案.
- 显著推进智能管道健康评估,并支持智能基础设施安全管理.
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