Bridge monitoring data suffers from noise interference,which affects the acquisition of the true re-sponse.However,traditional empirical mode decomposition methods have limited de-noise effects.In order to enhance the de-noise effect of monitoring data,a bridge monitoring data de-noise method based on time-varying filtered empirical mode decomposition(TVFEMD)and intrinsic mode function(IMF)energy entropy incre-ment is proposed.Firstly,the bridge monitoring data is decomposed using the TVFEMD to obtain a number of subseries.After that,the IMF energy entropy increment is used to determine the effective subsequence among several subseries.Then,the effective subsequences are recombined to achieve de-noise in the monitoring data.Finally,the effectiveness of the proposed method in terms of de-noise is evaluated using the mean absolute error(MAE),root mean square error(RMSE),and signal-noise ratio(SNR).The results of the simulation and en-gineering examples show that:The TVFEMD effectively solves the problem of mode mixing in the empirical mode decomposition(EMD);The combination of the TVFEMD with the IMF energy entropy increment method,which effectively suppresses the effects of multiple noise and provides significant improvements to the accuracy of the results;Compared with the EMD and the Kalman filtering methods,the MAE and the RMSE values are improved by more than 23％and 21％,respectively,which indicates that the proposed method is more effective in de-noise.The SNR value is improved by more than 38％,which demonstrates that the pro-posed method has superior noise immunity.
关键词
桥梁/健康监测/降噪/时变滤波经验模态分解/本征模函数能量熵增量
Key words
bridge/health monitoring/de-noise/time-varying filtered empirical mode decomposition/intrinsic mode function energy entropy increment
维普
万方数据