Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (4): 959-969.doi: 10.13229/j.cnki.jdxbgxb20200889

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Feature extraction method of pipeline signals based on VMD de-noising and dispersion entropy

Yi-na ZHOU1,2(),Hong-li DONG1,2,3,Yong ZHANG4,Jing-yi LU1,2,3()   

  1. 1.Artificial Intelligence Energy Research Institute，Northeastern Petroleum University，Daqing 163318，China
    2.Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control，Northeastern Petroleum University，Daqing 163318，China
    3.Sanya Offshore Oil & Gas Research Institute，Northeast Petroleum University，Sanya 572024，China
    4.Institute of Electronic Science，Northeastern Petroleum University，Daqing 163318，China
  • Received:2020-11-19 Online:2022-04-01 Published:2022-04-20
  • Contact: Jing-yi LU E-mail:1182090720@qq.com;ljywdm@126.com

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Abstract:

In view of the non-linear and non-stationary characteristics of pipeline acoustic signals and the difficulty of extracting the characteristics of pipeline leakage signal， a feature extraction method for acoustic signal was proposed. Firstly， the variational mode decomposition （VMD） was used to de-noise the collected acoustic signal， during which process the mode number of VMD was determined by the method of Minimum Bhattacharyya distance. Then the Wasserstein Distance （WD） between the probability density of the （Intrinsic Mode Function） IMF components was obtained by VMD and the original signal， which was evaluated to select the effective modes so as to reconstruct the selected effective modes. Finally， the dispersion entropy value of the reconstructed signal was used as the signal characteristic parameter， and the characteristic parameter was input into the extreme learning machine （ELM） to recognize the working condition. Experimental results show that the proposed method could classify and recognize pipeline signals accurately， and the total recognition rate is up to 100%.

Key words: pipeline leakage, variational mode decomposition, Wasserstein distance, dispersion entropy, feature extraction

CLC Number: 

  • TN911.72

Fig. 1

Flow chart of determining number of K-value based on Bdmin"

Fig.2

Flow chart of selecting effective mode component"

Fig.3

Flow chart of proposed method"

Fig.4

Time domain waveform and spectrum of threeworking conditions signals"

Table 1

Bdmin at different K"

KBdminKBdmin
20.006260.0064
30.009470.0062
40.012880.0000
50.009490.0000

Fig.5

Decomposition results of VMD"

Fig.6

WD of pdf between IMF and original signal"

Fig.7

Time domain waveform and spectrum diagram of de-noised leakage signal"

Fig.8

Denoised signals of pipeline under three conditions"

Fig.9

Dispersion entropy curve of denoised signal under different working conditions"

Table 2

Data set allocation"

管道工况训练集/个测试集/个类别标签
敲击80201
泄漏80202
正常80203

Fig.10

Classification results of test sets"

Table 3

Identification results of test sets"

方 法准确率/%
训练准确率测试准确率
VMD?DE?ELM100100
DE?ELM92.0890
NLM?DE?ELM99.1795
VMD?SSE?ELM77.576.67
VMD?IE?ELM97.0893.33
VMD?BE?ELM82.580
VMD?SE?ELM99.1798.33
VMD?ELM8575
ELM80.1773.33
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