基于FSDPC_Otsu算法的滚动轴承故障研究

doi:10.3969/j.issn.1000-1158.2021.11.09

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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.

Key words： metrology rolling bearing fault diagnosis FSDPC OTSU underdetermined blind separation

Received: 06 March 2020 Published: 01 December 2021

PACS:	TB936
	TH17

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	Articles by authors
	XING Ting-ting
	GUAN Yang
	SUN Deng-yun
	MENG Zong
	FAN Feng-jie

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XING Ting-ting,GUAN Yang,SUN Deng-yun, et al. Rolling Bearing Fault Diagnosis Based on Clustering by Fast Search and Find of Density Peaks Combined Otsu Method[J]. Acta Metrologica Sinica, 2021, 42(11): 1466-1471.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.11.09 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I11/1466

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