# [1]王海军,许飞云.基于非负Tucker 3分解的稀疏分量分析在故障信号提取中的应用[J].东南大学学报(自然科学版),2013,43(4):758-762.[doi:10.3969/j.issn.1001-0505.2013.04.016] 　Wang Haijun,Xu Feiyun.Sparse component analysis based on nonnegative Tucker 3 decomposition for fault signal extraction[J].Journal of Southeast University (Natural Science Edition),2013,43(4):758-762.[doi:10.3969/j.issn.1001-0505.2013.04.016] 点击复制 基于非负Tucker 3分解的稀疏分量分析在故障信号提取中的应用() 分享到： var jiathis_config = { data_track_clickback: true };

43

2013年第4期

758-762

2013-07-20

## 文章信息/Info

Title:
Sparse component analysis based on nonnegative Tucker 3 decomposition for fault signal extraction

Author(s):
School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Keywords:

TH17;TP206
DOI:
10.3969/j.issn.1001-0505.2013.04.016

Abstract:
Aiming at the problem of non-sparseness of original diagnosis signal and being difficult to distinguish, a method of sparse component analysis(SCA)based on nonnegative Tucker 3 decomposition(NTD)is proposed to process the quadratic feature of faults for improving the sparseness. Meanwhile, a new updating algorithm with nonnegative constraints is put forward to overcome the limitation of slow convergence and data overfitting in NTD. Compared with the conventional alternative least squares(ALS), the updating algorithm can simultaneously compute all the factors in one time that avoids calculating the large-scale Jacobian matrix and therefore improves the efficiency. The experimental results show that the method of combining NTD and SCA(SCA_NTD)only needs 150 steps to achieve convergence which is superior to other typical methods like NTF, etc; SCA_NTD also gets the highest accuracy of 97.16% under the same dimension of a tensor. Therefore, SCA_NTD not only improves the sparseness but also is significant to improve the convergence and efficiency.

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