[1]李衡,康维新.基于EMD与模糊聚类的桩缺陷特征提取与识别[J].应用科技,2019,46(02):88-93.[doi:10.11991/yykj.201803015]
 LI Heng,KANG Weixin.Pile defect feature extraction and identification based on EMD and fuzzy clustering[J].Applied science and technology,2019,46(02):88-93.[doi:10.11991/yykj.201803015]
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基于EMD与模糊聚类的桩缺陷特征提取与识别(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第46卷
期数:
2019年02期
页码:
88-93
栏目:
计算机技术与应用
出版日期:
2019-03-05

文章信息/Info

Title:
Pile defect feature extraction and identification based on EMD and fuzzy clustering
作者:
李衡 康维新
哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001
Author(s):
LI Heng KANG Weixin
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
桩基|经验模态分解|信息熵|模糊聚类|缺陷|无损检测|特征提取|识别
Keywords:
pile foundation|EMD|information entropy|fuzzy clustering|defect|non-destructive testing|feature extraction|identification
分类号:
TU391.4
DOI:
10.11991/yykj.201803015
文献标志码:
A
摘要:
针对桩基缺陷信号特征提取困难的问题,对缺陷信号进行经验模态分解(EMD)并求取信息熵,构建了一种基于信息熵的均值特征向量;针对特征向量元素数较多,不能真实反映信号的特征以及过多的特征影响识别效率的问题,引入模糊聚类的相关技术方法,对均值特征向量进行相空间重构,对重构矩阵进行模糊聚类,根据聚类的结果构建新的特征向量。通过实验仿真证明了该特征向量取得了很好的识别效果,具有实际应用价值,可以作为一种有效的特征类型;同时验证了通过模糊聚类进行特征向量降维的有效性。
Abstract:
In this paper, according to the difficulty in feature extraction of pile foundation defect signals, the defect signals are firstly decomposed by empirical mode decomposition (EMD) and the information entropy is obtained. A mean value feature vector based on information entropy is constructed. Aiming at the problem that a large number of eigenvector elements cannot truly reflect the characteristics of the signal and too many features have affected the recognition efficiency, a theory of fuzzy clustering is introduced to phase space reconstruction of the mean value feature vector, and fuzzy clustering of the reconstruction matrix is performed, and further new eigenvectors are generated based on the clustering results. Through experimental simulation, it is found that the feature vector has a good recognition effect, and thus has practical application value and can be used as an effective feature type; at the same time, it validates effectiveness of the feature vector dimension reduction through fuzzy clustering.

参考文献/References:

[1] ALI J B, FNAIECH N, SAIDI L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied acoustics, 2015, 89:16-27.
[2] 刘明贵, 岳向红, 杨永波, 等. 基于Sym小波和BP神经网络的基桩缺陷智能化识别[J]. 岩石力学与工程学报, 2007, 26(S1):3484
[3] 何岭松, 李巍华. 用Morlet小波进行包络检波分析[J]. 振动工程学报, 2002, 15(1):119-122
[4] 朱艳萍, 包文杰, 涂晓彤, 等. 改进的经验小波变换在滚动轴承故障诊断中的应用[J]. 噪声与振动控制, 2018, 38(1):199-203
[5] 刘金朝, 丁夏完, 王成国. 自适应共振解调法及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2007, 26(1):38-41
[6] 王平, 廖明夫. 滚动轴承故障诊断的自适应共振解调技术[J]. 航空动力学报, 2005, 20(4):606-612
[7] 邴智刚, 李威霖, 陈锋, 等. 基于虚拟仪器的旋转机械主轴故障在线监测系统研究[J]. 机电工程, 2016, 33(6):722-726
[8] LI Zipeng, CHEN Jinglong, ZI Yanyang, et al. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive[J]. Mechanical systems and signal processing, 2017, 85:512-529.
[9] 张志刚, 石晓辉, 陈哲明, 等. 基于改进EMD与滑动峰态算法的滚动轴承故障特征提取[J]. 振动与冲击, 2012, 31(22):80-83
[10] DJEBALA A, BABOURI M K, OUELAA N. Rolling bearing fault detection using a hybrid method based on empirical mode decomposition and optimized wavelet multi-resolution analysis[J]. The international journal of advanced manufacturing technology, 2015, 79(9/10/11/12):2093-2105.
[11] SUN Zehang, BEBIS G, MILLER R. Object detection using feature subset selection[J]. Pattern recognition, 2004, 37(11):2165-2176.
[12] KARAMIZADEH S, ABDULLAH S M, ZAMANI M, et al. Pattern recognition techniques:studies on appropriate classifications[M]//SULAIMAN H, OTHMAN M, OTHMAN M, et al. Advanced Computer and Communication Engineering Technology. Cham:Springer, 2015:791-799.
[13] 孟宗, 李姗姗, 王亚超. 基于LMD和局域时频熵的旋转机械故障诊断方法[J]. 计量学报, 2015, 36(1):77-81
[14] RUSPINI E H. A new approach to clustering[J]. Information and control, 1969, 15(1):22-32.
[15] BEZDEK J C. Objective function clustering[M]//Pattern recognition with fuzzy objective function algorithms. Boston, MA:Springer, 1981:43-93.
[16] BEZDEK J C. A physical interpretation of fuzzy ISODATA[M]//DUBOIS D, PRADE H, YAGER R R. Readings in Fuzzy Sets for Intelligent Systems. San Francisco, USA:Morgan Kaufmann Pub, 1993:615-616.
[17] BEZDEK J C, HATHAWAY R J, SABIN M J, et al. Convergence theory for fuzzy c-means:counterexamples and repairs[J]. IEEE transactions on systems, man, and cybernetics, 1987, 17(5):873-877.
[18] PAL N R, BEZDEK J C. On cluster validity for the fuzzy c-means model[J]. IEEE Transactions on fuzzy systems, 1995, 3(3):370-379.

备注/Memo

备注/Memo:
收稿日期:2018-03-29。
基金项目:国家自然科学基金项目(61371174)
作者简介:李衡,男,硕士研究生;康维新,男,教授,博士
通讯作者:李衡,E-mail:lihengcice@hrbeu.edu.cn
更新日期/Last Update: 2019-03-06