基于不确定性的融合方法用于结构模态参数识别
Xiaoteng Liu1, Zirui Dong2, Hongxia Ji2
1College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了一种基于不确定性的融合方法,用于结构模态参数识别,结合时间域和频域方法. 这种新技术提高了结构模态分析的准确性,并减少了结构模态分析中的错误.
科学领域:
- 结构动力学和振动分析.
- 计算力学和工程.
背景情况:
- 结构模态参数识别方法对于理解结构行为至关重要.
- 现有的时间域和频域方法具有明显的优势,但容易受到测量噪声和统计不确定性的影响.
- 模态参数 (例如自然频率,减噪比率,模态形状) 对于结构健康监测和性能评估至关重要.
研究的目的:
- 开发一种以不确定性为基础的融合方法,用于结构模式参数的识别.
- 合并时间域和频域识别技术的互补优势.
- 通过减轻统计不确定性,提高模式参数估计的可靠性和准确性.
主要方法:
- 建议使用统一的参数模型来表达自回归 (AR) 和左矩阵分数 (LMF) 模型.
- 为识别模态参数和计算相关差异而开发了一个通用框架.
- 模态参数估计是通过不同方法的结果的反变量加权总和来计算的.
主要成果:
- 拟议的融合方法可提供可靠的模态参数估计.
- 该方法显著减少了大量估计错误的发生.
- 证明了增强的识别能力,有效地降低了缺失结构模式的可能性.
结论:
- 基于不确定性的融合方法为结构性模式识别提供了强大的方法.
- 这种技术有效地结合了各种模式分析方法的优势.
- 拟议的框架提高了结构工程中模式参数识别的准确性和完整性.
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