基于贝叶斯的超参数优化1D-CNN用于结构异常检测.
Xiaofei Li1, Hainan Guo1, Langxing Xu1
1College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种优化的深度学习策略,用于使用贝叶斯算法和数据融合进行结构损伤诊断. 即使使用稀疏的传感器,该方法也能达到高精度 (99.85%),改善了结构健康监测.
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
- 数据科学数据科学数据科学
背景情况:
- 大规模的结构健康监测数据需要先进的分析技术.
- 深度学习模型显示出诊断结构异常的前景,但需要复杂的超参数调整.
- 目前的超参数调整方法往往是主观的,基于经验.
研究的目的:
- 为各种结构损伤诊断提出构建和优化1D-CNN模型的新策略.
- 为了提高模型在不同结构检测场景中的适用性.
- 为了克服传统的主观超参数调整方法的局限性.
主要方法:
- 开发了一种用于构建和优化1D-CNN模型的策略,使用贝叶斯对超参数的优化.
- 集成数据融合技术,以提高模型识别精度.
- 应用该方法来监测整个结构,即使有稀疏的传感器测量点.
主要成果:
- 在一个简单支的光束测试案例中,在小局部元素中实现了参数变化的高效和准确的识别.
- 在公开可用的结构数据集上验证了方法的稳定性,达到99.85%的识别准确性.
- 在传感器占用率,计算成本和识别精度方面,与现有方法相比,证明了显著的优势.
结论:
- 拟议的战略为结构损坏诊断提供了一个强大而准确的方法.
- 该方法提高了深度学习模型在结构健康监测中的应用性.
- 这种方法为传统的超参数调提供了更客观,更有效的替代方案.
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