Application of signal sparse decomposition theory in bearing fault detection

被引：2

作者：

Zhang X. ^{[1
]}

Hu N. ^{[1
]}

Cheng Z. ^{[1
]}

Hu L. ^{[1
]}

Chen L. ^{[1
]}

机构：

[1] Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha

来源：

Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2016年 / 38卷 / 03期

关键词：

Bearing fault detection; Dictionary learning; Sparse decomposition; Sparse representation error;

D O I：

10.11887/j.cn.201603024

中图分类号：

学科分类号：

摘要：

A new bearing fault detection method based on the signal sparse decomposition theory was developed. An over-complete dictionary on which the bearing vibration signals in normal state can be represented sparsely was trained by the dictionary learning method. According to the fact that this dictionary just can sparsely represent the signals in normal state, the bearing vibration signal in unknown state was decomposed on this dictionary. The bearing state was determined by comparing the representation error of the signal on the dictionary with the given error threshold, and then the bearing fault detection was achieved. Experimental tests validate the effectiveness of the proposed method in bearing fault detection when setting an appropriate error threshold. © 2016, NUDT Press. All right reserved.

引用

页码：141 / 147

页数：6

共 24 条

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