Orthogonal wavelet transform KCA in fault diagnosis
LI Weipeng, CAO Yan, LI Lijuan
1. School of Mechanical and Electrical Engineering, Xi’an Technological University, Xi’an 710600, China;
2. School of Intelligent Manufacturing, Nanyang Institute of Technology, Nanyang 473004, China
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.
李卫鹏,曹岩,李丽娟. 正交小波变换k-中心点聚类算法在故障诊断中的应用[J]. 振动与冲击, 2021, 40(7): 291-296.
LI Weipeng, CAO Yan, LI Lijuan. Orthogonal wavelet transform KCA in fault diagnosis. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(7): 291-296.
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