基于快速集合经验模态分解的桥梁动态特性分析

Dynamic Characteristics Analysis of Bridges Based on Fast Set Empirical Mode Decomposition

GNSS技术与传统的桥梁变形检测手段相比,具备实时持续监控、受气候环境影响较小、高精度、高采样率、自动化、全天候的优点,基于GNSS的长时间的高采样率监测,可提供桥梁结构的动态特性信息。对受环境因素等影响含有大量噪声的GNSS桥梁结构变形监测坐标时间序列进行精度频率等特性的识别提取,是该类大型工程安全运营的关键。通过假设一组模拟仿真信号数据为例,采用快速集合经验模态分解(FEEMD)数据分解算法,获取能反映桥梁安全运行状态的频率等特性。

GNSS technology has the advantages of real-time continuous monitoring,less impact by climate and environment,high accuracy,high sampling rate,automation and all-weather.Long-time high sampling rate monitoring based on GNSS can provide dynamic characteristics information of bridge structures.It is a key issue for the safe operation of such large-scale projects to identify and extract the characteristics of the accuracy and frequency of the GNSS bridge structure deformation monitoring coordinate time series affected by environmental factors and other factors that contain a lot of noise.In this paper,by assuming a group of simulated signal data as an example,the fast set empirical mode decomposition(FEEMD)data decomposition algorithm is adopted to obtain the frequency and other characteristics that can reflect the safe operation status of the bridge.