Abstract：To solve the problem of underdetermined blind separation,caused by unknown vibration sources and smaller number of observation signals, an improved method of clustering by fast search and find of density peaks (FSDPC) is proposed. Initially, the mixed signal is projected to the multi-dimensional space and then calculate each point density value, using the Otsu method following for density threshold segmentation so as to remove the influence of interference on the accuracy of clustering,and then determine the cluster center according to the data density peaks, to estimate the mixing matrix;finally utilizing L1 norm minimization to separate mixed signals,and the envelope spectrum analysis is carried to realize fault diagnosis. The FSDPC_Otsu method can estimate the mixed matrix under the condition of the unknown source number and the initial value of the cluster center, at the same time, the accuracy of the mixed matrix can be guaranteed. The experimental results show that the sparse component analysis of the FSDPC_Otsu can separate the multiple fault signals of the bearing and realize the fault diagnosis.
邢婷婷,关阳,孙登云,孟宗,樊凤杰. 基于FSDPC_Otsu算法的滚动轴承故障研究[J]. 计量学报, 2021, 42(11): 1466-1471.
XING Ting-ting,GUAN Yang,SUN Deng-yun,MENG Zong,FAN Feng-jie. Rolling Bearing Fault Diagnosis Based on Clustering by Fast Search and Find of Density Peaks Combined Otsu Method. Acta Metrologica Sinica, 2021, 42(11): 1466-1471.
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2021, Vol. 42 
