Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (1): 216-226.doi: 10.13229/j.cnki.jdxbgxb20190013

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Bridge vibration signal optimization filtering method based on improved CEEMD⁃multi⁃scale permutation entropy analysis

Bo-xin WANG1(),Hai-tao YANG1,Qing WANG1,Xin GAO1(),Xiao-xu CHEN2   

  1. 1. College of Construction Engineering, Jilin University, Changchun 130021, China
    2. Changchun Smart City Science and Technology Co. Ltd. , Changchun 130033, China
  • Received:2019-01-04 Online:2020-01-01 Published:2020-02-06
  • Contact: Xin GAO E-mail:boxinwang@jlu.edu.cn;gao_xin@jlu.edu.cn

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Abstract:

To solve the problem of bridge vibration signal with high degree of non-stationary feature, an optimization filtering method of adaptive decomposition and reconstruction is proposed for the bridge health monitoring. On the basis of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the original signal is decomposed into multiple intrinsic mode functions (IMFs) with different feature time-scales. The endpoint mirroring method is used to suppress endpoint effect. In order to find the IMFs with high randomness, the multi-scale permutation entropy (MPE) is introduced to analyze the mean entropy value of each IMF at different scales. Then the noise component and abnormal component are respectively eliminated to complete the first filtering. In order to achieve better similarity and smoothness for the final filter, the optimized reconstruction model is established to complete the second filtering. The research shows that adaptive decomposition stage of the proposed method has better completeness, orthogonality and computational efficiency than traditional ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD) methods. Meanwhile, the proposed method suppresses the mode mixing phenomenon to a certain extent, and the endpoint effect is improved. Through the optimized reconstruction, the final filter signal has high signal-to-noise ratio. Finally, the measured signal analysis once again proves the advantages of the proposed method, the filter result can be used as the basis of bridge health monitoring technology.

Key words: bridge engineering, bridge health monitoring, decomposition and reconstruction, filtering, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)

CLC Number: 

  • U448.27

Fig.1

"

Fig.2

Simulated signal"

Table 1

Simulated signal basic condition setting"

方 法 加噪次数 加噪幅值 迭代次数
EEMD 200 0.2 100
CEEMD 100(对)×2 0.2 100
本文 200 0.2 100

Fig.3

"

Fig.4

MPE mean value distribution in each IMF"

Fig.5

Final decomposition of proposed method"

Table 2

EEMD decomposition IMF correlation analysis"

s1 s2 s3
IMF1 -0.002 5 -0.001 5 0.003 7
IMF2 -0.001 3 0.000 2 0.002 7
IMF3 0.004 1 0.008 2 0.019 3
IMF4 0.005 5 0.106 9 0.896 3
IMF5 -0.058 4 0.397 7 0.894 7
IMF6 0.243 9 0.937 4 0.019 6
IMF7 0.983 0 0.023 4 0.015 6
IMF8 0.320 9 0.021 3 0.019 2
IMF9 0.054 9 0.028 1 0.019 5
RS 0.088 6 0.045 1 0.030 1

Table 3

CEEMD decomposition IMF correlation analysis"

s1 s2 s3
IMF1 -0.001 9 -0.001 9 0.003 4
IMF2 -0.001 2 -0.000 3 0.002 8
IMF3 0.006 0 0.011 0 0.018 3
IMF4 0.002 7 0.098 0 0.907 6
IMF5 -0.065 3 0.431 5 0.877 8
IMF6 0.287 2 0.921 9 0.018 8
IMF7 0.981 4 0.019 7 0.014 2
IMF8 0.311 1 0.023 3 0.019 8
RS 0.102 9 0.052 3 0.035 2

Table 4

Proposed method decomposition IMF correlation analysis"

s1 s2 s3
IMF1 0.069 7 0.473 6 0.861 3
IMF2 0.098 4 0.953 4 0.001 9
IMF3 0.972 5 0.027 3 0.024 1
IMF4 0.049 0 0.022 8 0.014 8
IMF5 0.032 5 0.016 0 0.010 6
IMF6 0.033 4 0.016 6 0.011 1
RS 0.032 4 0.016 2 0.010 8

Fig.6

"

Table 5

Evaluation indicators for different methods"

方 法 Mse t/s Ort
EEMD 0.003 3 324.5855 0.216 8
CEEMD 0.002 8 294.0562 0.222 4
本文 4.103 8×10-5 50.1826 0.103 5

Table 6

Filter signal analysis of proposed method"

相似度 光滑度 目标函数
LB 1 0.419 2 0.007 0 0.997 3
LB 2 0.350 3 0.007 0 0.917 1
LB 3 0.190 3 0.007 1 0.727 0
LB 4 0.189 3 0.007 1 0.725 8
LB 5 0.187 2 0.007 1 0.723 3
LB 6 0.187 3 0.007 1 0.723 4
LB 7 0.185 8 0.007 1 0.721 7

Fig.7

Simulation signal optimization filter"

Table 7

Real signal basic condition setting"

方法 加噪次数 加噪幅值 迭代次数
EEMD 300 0.2 200
CEEMD 150(对)×2 0.2 200
本文 300 0.2 200

Fig.8

Real signal decomposition results for each method"

Table 8

Evaluation indicators for each methods"

方 法 t /s Ort dnSNR
EEMD 936.8431 0.2184 9.1537
CEEMD 880.8714 0.2135 9.1488
本文方法 82.9216 0.0063 6.0958

Fig.9

Unilateral spectrum of each method filters"

Fig.10

Comparison between original signal and optimized filter of proposed method"

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