Pipeline defect recognition method based on CEEMD-FCM

doi:10.16265/j.cnki.issn1003-3033.2020.01.014

Abstract

Abstract: In order to improve identification accuracy of pipeline defects, CEEMD-FCM model is proposed by using CEEMD and FCM clustering algorithm. Firstly, based on analysis of defect signal waveform features, particle swarm optimization algorithm (PSO) was introduced to improve wavelet threshold de-noising method, and noise reduction of pipeline defect signals was realized. Then, defect signals were decomposed by CEEMD, and characteristic parameters of defects were extracted through principle of energy entropy. Finally, FCM was optimized by simulating annealing algorithm (SA) and genetic algorithm (GA) to complete classification of pipeline defects. The results show that comprehensive identification accuracy of the proposed identification method reaches 87.5%. It can achieve accurate identification of defect modes in petrochemical industry, therefore ensuing safe operation of pipeline and reducing accident rates.

Key words: pipeline, defect type identification, feature extraction, complete ensemble empirical mode decomposition (CEEMD), fuzzy C-means(FCM) clustering algorithm

CLC Number:

X937

WANG Chaoqun, LIANG Wei, LIANG Xiaobin. Pipeline defect recognition method based on CEEMD-FCM[J]. China Safety Science Journal, 2020, 30(1): 87-93.

References

[1] 洪仁植,王树达,常亮.基于BP神经网络的管道缺陷模式识别与精确定量识别[J]. 东北石油大学学报,2008, 32(1): 83-85.
HONG Renzhi,WANG Shuda,CHANG Liang. Pipe defect pattern recognition and accurate quantitative recognition based on BP neural network[J]. Journal of Northeast Petroleum University, 2008, 32(1): 83-85.
[2] 王思文.经验模态分解及其在降噪方面的应用[J]. 机械制造,2011, 38(10): 26-29.
WANG Siwen.Experiential mode decomposition and its application in noise reduction[J]. Machine Manufacturing, 2011, 38(10): 26-29.
[3] 姜万录,卢传奇,朱勇.基于HHT和模糊C均值聚类的轴向柱塞泵故障识别[J].吉林大学学报:工学版,2015,45(2): 429-436.
JIANG Wanlu, LU Chuanqi, ZHU Yong.A fault identification of axial piston pump based on HHT and fuzzy C-means clustering[J].Journal of Jilin University:Engineering Science,2015,45(2): 429-436.
[4] XU Fan,TSE W T P. Roller bearing fault diagnosis using stacked denoising autoencoder in deep learning and Gath-Geva clustering algorithm without principal component analysis and data label[J]. Applied Soft Computing, 2018, 73: 898-913.
[5] 张立国,康乐,金梅,等.一种基于EEMD-SVD和FCM的轴承故障诊断方法[J].计量学报,2016,37(1): 67-70.
ZHANG Liguo,KANG Le,JIN Mei,et al. A bearing fault diagnosis method based on EEMD-SVD and FCM[J].Acta Metrologica Sinica,2016,37(1): 67-70.
[6] 王朝晖,姚德群,段礼祥.基于模糊聚类的油田往复压缩机气阀故障诊断研究[J]. 机械强度,2007, 29(3): 521-524.
WANG Zhaohui, YAO Dequn, DUAN Lixiang. Fault diagnosis of gas valves for oil field reciprocating compressors based on fuzzy clustering[J]. Mechanical Strength, 2007, 29(3): 521-524.
[7] ASKARI S, MONTAZERIN N, ZARANDI M H F, et al. Generalized entropy based possibilistic fuzzy C-means for clustering noisy data and its convergence proof[J]. Neuro-computing, 2016, 219: 186-202.
[8] 凌鹏,陈跃威.基于CEEMD互近似熵和FCM滚动轴承故障诊断[J].计算机仿真,2018,35(3):314-318,410.
LING Peng,CHEN Yuewei. Based on CEEMD mutual approximation entropy and FCM rolling bearing fault diagnosis[J].Computer Simulation,2018,35(3):314-318,410.
[9] YEH J R, SUN W Z, SHIEH J S, et al. Investigating fractal property and respiratory modulation of human heartbeat time series using empirical mode decomposition [J]. Medical Engineering & Physics, 2010, 32(5): 490-496.
[10] ABDELKADER R, KADDOUR A, BENDIABDELLAH A, et al. Rolling bearing fault diagnosis based on an improveddenoising method using the complete ensemble empirical mode decomposition and the optimized threshold ingoperation [J].IEEE Sensors Journal, 2018, 18(17): 7 166-7 172.
[11] KUMAR P, SIROHI D. Comparative analysis of FCM and HCM algorithm on iris data set [J]. International Journal of Computer Applications, 2010, 5(2): 33-37.
[12] 曾山,同小军,桑农,等.基于对应分析的冗余模糊C均值聚类算法研究[J]. 华中科技大学学报:自然科学版, 2012, 40(2): 107-111.
ZENG Shan, TONG Xiaojun, SANG Nong, et al. Research on redundant fuzzy C-means clustering algorithm based on correspondence analysis[J]. Journal of Huazhong University of Science and Technology:Natural Science, 2012, 40(2): 107-111.

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