CAJ | 학술논문

针对桥梁结构动力测试信号噪声水平高、难以分离结构有效信号的特点，在总体平均经验模态分解方法和主成分分析的基础上，建立了自适应分解与重构方法。对经验模态分解结果的模态混叠现象进行深入分析，利用白噪声概率密度函数的均匀性对模态混叠模式一进行了改进，基于相关性分析改进了模态混叠模式二，改进后的分解方法在计算效率和分解精度上均有较大提升；随后对所有分解获得的固有模态函数进行多尺度主成分分析，实现降噪和选择并重构测试信号。分别用模拟信号和实际桥梁测试信号对所提方法的有效性进行了验证。结果表明：改进后的信号自适应分解和重构方法能在降噪的同时，有效地提取桥梁结构信息，可用于实际桥梁结构的动力测试分析中。
침대교량결구동력측시신호조성수평고、난이분리결구유효신호적특점，재총체평균경험모태분해방법화주성분분석적기출상，건립료자괄응분해여중구방법。대경험모태분해결과적모태혼첩현상진행심입분석，이용백조성개솔밀도함수적균균성대모태혼첩모식일진행료개진，기우상관성분석개진료모태혼첩모식이，개진후적분해방법재계산효솔화분해정도상균유교대제승；수후대소유분해획득적고유모태함수진행다척도주성분분석，실현강조화선택병중구측시신호。분별용모의신호화실제교량측시신호대소제방법적유효성진행료험증。결과표명：개진후적신호자괄응분해화중구방법능재강조적동시，유효지제취교량결구신식，가용우실제교량결구적동력측시분석중。
In order to extract structural information from bridge structural dynamic signals with high noise level,a novel adaptive decomposition and reconstruction method was proposed by combining the ensemble empirical mode decomposition (EEMD)method and the principal component analysis (PCA)method for the specific characteristics of bridge structural dynamic signals. Based on the in-depth analysis of mode mixing in results of empirical mode decomposition,the uniformity of probability density function of white noise was adopted to improve the pattern I of mode mixing,and the correlation analysis was used to ameliorate the pattern II of mode mixing,then the calculation efficiency and decomposition accuracy were upgraded greatly for the improved EEMD.The multi-scale principal components analysis was implemented for all of the intrinsic mode functions (IMFs)obtained with the improved EEMD to reduce noise and select IMFs.Moreover,the dynamic signals were reconstructed.The effectiveness of the proposed method was verified with both the simulated signals and testing signals from real bridge structures.The results showed that the proposed method can be used to decompose adaptively and denoise effectively the bridge dynamic signals with high noise,and extract accurately the structural information from the testing signals,furthermore,it is applicable for the dynamic testing analysis of real bridge structures.