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基于改进的希尔伯特 - 黄变换的紧密间隔模式的土木结构的运行模式分析
Xu-Qiang Shang1, Tian-Li Huang1, Yi-Bin He2
1School of Civil Engineering, Central South University, Changsha 410075, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究引入了使用分析模式分解 (AMD) 的改进的希尔伯特-黄变换 (HHT),用于在模式距离近的结构中准确地识别模态参数,克服经验模式分解 (EMD) 的局限性. 该方法增强了桥梁和建筑物的自然频率和阻尼比率估计.
科学领域:
- 结构动力学和振动分析.
- 用于土木工程应用的先进信号处理技术.
背景情况:
- 在长跨度桥梁和高层建筑中距离较近的模式对模态参数的识别构成了挑战.
- 在希尔伯特 - 黄变换 (HHT) 中的实证模式分解 (EMD) 在密切间隔的模式中与模式分离作斗争.
- 准确的模式识别对于结构健康监测和绩效评估至关重要.
研究的目的:
- 提出并验证使用分析模式分解 (AMD) 来准确识别模态参数的改进的HHT方法.
- 解决传统的HHT方法在分析结构时存在的局限性.
- 为了提高自然频率和减噪比率估计的精度.
主要方法:
- 用分析模式分解 (AMD) 取代经验模式分解 (EMD) 以将结构响应分解为单元模式.
- 将随机递减技术应用于分解模式,以获得自由衰变反应.
- 在自由衰变反应上使用希尔伯特变换 (HT) 进行模态参数估计.
- 验证了使用三度自由度系统,一高楼和廷高桥的方法.
主要成果:
- 提出的基于AMD的HHT方法成功将响应分解为单元模式.
- 对于具有相距较近的模式的结构,可以准确识别自然频率和阻尼比率.
- 与现有的模式识别技术相比,该方法显示出更高的精度.
- 对各种结构的验证证实了该方法的准确性,有效性和适用性.
结论:
- 基于AMD的改进的HHT方法提供了一个高效和准确的解决方案,用于在模式相距较近的结构中识别模态参数.
- 这种方法克服了传统基于EMD的HHT的模式分离缺陷.
- 这些发现为复杂结构的动态分析和健康监测提供了更可靠的工具.
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