Orthogonal wavelet transform KCA in fault diagnosis

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (7) : 291-296.

LI Weipeng， CAO Yan， LI Lijuan

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Abstract

k-medoids cluster algorithm(KCA) is an improved machine learning clustering algorithm. This method reveals the inherent properties and laws of the data,through selecting the initial clustering center ,updating the clustering center and learning the unmarked training samples, so as to distinguish the running state of the machine. In this paper, an orthogonal wavelet transform k-medoids clustering algorithm (OWTKCA) was proposed for diagnosis, which uses the orthogonal wavelet transform (OWT) method to extract the detailed signals as training samples, and uses the KCA method to classify them.The results of test data classification of rolling bearing show that this method can deal with complex mechanical vibration signals more effectively than KCA without extracting characteristic values, it obviously improves the clustering effect of fault data, shortens the clustering time and improves the efficiency of intelligent diagnosis.

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k-medoids cluster algorithm(KCA) / machine learning / fault diagnosis / orthogonal wavelet transform(OWT)

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LI Weipeng， CAO Yan， LI Lijuan. Orthogonal wavelet transform KCA in fault diagnosis[J]. Journal of Vibration and Shock, 2021, 40(7): 291-296

References

［1］HAN J W,KAMBER M.Data mining: concepts and techniques［M］.Beijing:China Machine Press,2000.
［2］ALOISE D, DESHPANDE A, HANSEN P, et al. NP-hardness of Euclidean sum-of-squares clustering［J］. Machine Learning,2009,75(2):245-248.
［3］PARK H S,JUN C H. A simple and fast algorithm for K-medoids clustering［J］. Expert Systems with Applications,2009,36(2):3336-3341.
［4］JAIN A K. Data clustering: 50 years beyond K-means［J］. Pattern Recognition Letters,2009, 31(8):651-666.
［5］JAIN A K, DUBES R C. Algorithms for clustering data［M］. Englewood Cliffs: Prentice Hall, Upper Saddle River, 1988.
［6］HUANG Z X. Extensions to the k-means algorithm for clustering large data sets with categorical values［J］. Data Mining and Knowledge Discovery,1998,2(3):283-304.
［7］蒋佳炜,胡以怀.基于小波包特征提取和模糊熵特征选择的柴油机故障分析［J］.振动与冲击，2020,39(4):273-277.
JIANG Jiawei,HU Yihuai.Fault analysis of diesel engine based on wavelet packet feature extraction and fuzzy entropy feature selection ［J］. Journal of Vibration and Shock, 2020,39(4):273-277.
［8］刘晓波,张明明,涂俊超,等.基于广度优先搜索的小波聚类算法［J］.振动与冲击,2016,35(15):178-183.
LIU Xiaobo, ZHANG Mingming, TU Junchao, et al.Wavelet clustering algorithm based on breadth first search ［J］. Journal of Vibration and Shock,2016,35(15):178-183.
［9］潘玉娜,陈进.小波包-支持向量数据描述在轴承性能退化评估中的应用研究［J］.振动与冲击，2009，28(4): 164-167.
PAN Yuna, CHEN Jin.Application of wavelet packet-support vector data description in performance degradation assessment of bearings ［J］.Journal of Vibration and Shock, 2009,28(4): 164-167.
［10］刘晓波,韩子东,邵伟芹,等.基于散列函数的双网格校正交小波聚类算法［J］.振动与冲击,2018,37(21):267-272.
LIU Xiaobo, HAN Zidong, SHAO Weiqin, et al.Double grid correction crossover wavelet clustering algorithm based on hash function ［J］. Journal of Vibration and Shock, 2012,37(21):267-272.
［11］吴文亮.聚类分析中k-均值与k-中心点算法的研究［D］.广州:华南理工大学，2011. 
［12］KAUFMAN L, ROUSSEEUW P J. Clustering by means of medoide［M］//Statistical Data Analysis Based on the L1 Norm and Related Methods. Amsterdam: ［s.n.］: 1987.
［13］李凌均，张周锁，何正嘉．基于支持向量数据描述的机械故障诊断研究［J］．西安交通大学学报，2003, 37(9): 910-913.
LI Lingjun, ZHANG Zhousuo, HE Zhengjia. Research of mechanical system fault diagnosis based on support vector data description［J］.Journal of Xi’an University, 2003, 37(9): 910-913.
［14］THEODORIDIS S,KOUTROUMBAS K.Pattern teconition［M］.Boston:Academic Press,2009.
［15］SCHOLKOPF B, SMOLA A, MILLER K R. Nonliear component analysis as a kernel eigenvalue problem［J］. Neural Computation, 1998,10(5): 1299-1319.
［16］谢娟英,郭文娟,谢维信.基于邻域的k中心点聚类算法［J］.陕西师范大学学报(自然科学版),2012,40(41):6-22.
XIE Juanying, GUO Wenjuan, XIE Weixin. Neighborhood based k-center clustering algorithm ［J］. Journal of Shaanxi Normal University (Natural Science Edition), 2012,40(41): 6-22.
［17］朱继梅.小波变换及其工程应用(连载)［J］.振动与冲击,1996,15(4):85-91.
ZHU Jimei.Wavelet transform and its engineering application (serial)［J］. Journal of Vibration and Shock,1996,15(4):85-91.
［18］孔凡,李杰.周期广义谐和小波变换及重构［J］.振动与冲击,2013,32(7):24-29.
KONG Fan, LI Jie.Periodic generalized harmonic wavelet transform and reconstruction ［J］. Journal of Vibration and Shock,2013,32(7):24-29.
［19］李帅位,张栋良,黄昕宇,等.数据特征选择与分类在机械故障诊断中的应用［J］.振动与冲击,2020,39(2):218-222.
LI Shuaiwei, ZHANG Dongliang, HUANG Xinyu, et al.Application of data feature selection and classification in mechanical fault diagnosis ［J］. Journal of Vibration and Shock,2020,39(2):218-222.
［20］李凌均,韩捷,李卫鹏,等.正交小波变换支持向量数据描述在轴承性能评估中的应用［J］.机械科学与技术,2012,31(7):1201-1204.
LI Lingjun, HAN Jie, LI Weipeng, et al. Applying orthogonal wavelet transform-SVDD to evaluating performance of bearing［J］. Mechanical Science and Technology for Aerospace Engineering, 2012, 31(7): 1201-1204.
［21］王宏超,陈进,董广明.基于补偿距离评估-小波核PCA的滚动轴承故障诊断［J］.振动与冲击,2013,32(18):87-90.
WANG Hongchao, CHEN Jin, DONG Guangming.Rolling bearing fault diagnosis based on compensating distance evaluation-wavelet core PCA ［J］. Journal of Vibration and Shock,2013,32(18):87-90.