[1] Peng Sen, Xu Feiyun, Jia Minping, Hu Jianzhong, et al. Sparseness-controlled non-negative tensor factorizationand its application in machinery fault diagnosis [J]. Journal of Southeast University (English Edition), 2009, 25 (3): 346-350. [doi:10.3969/j.issn.1003-7985.2009.03.013]

Copy

Sparseness-controlled non-negative tensor factorizationand its application in machinery fault diagnosis()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 3

Page:

346-350

Research Field:

Automation

Publishing date:

2009-09-30

Info

Title:

Sparseness-controlled non-negative tensor factorizationand its application in machinery fault diagnosis

Author(s):

Peng Sen;  Xu Feiyun;  Jia Minping;  Hu Jianzhong

School of Mechanical Engineering, Southeast University, Nanjing 211189, China

Keywords:

non-negative tensor factorization;  sparseness;  feature extraction;  bispectrum;  gearbox

PACS:

TP206+.3

DOI:

10.3969/j.issn.1003-7985.2009.03.013

Abstract:

Aiming at the problems of bispectral analysis when applied to machinery fault diagnosis, a machinery fault feature extraction method based on sparseness-controlled non-negative tensor factorization(SNTF)is proposed.First, a non-negative tensor factorization(NTF)algorithm is improved by imposing sparseness constraints on it.Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor.Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor.Finally, coefficients indicating these basis images’ weights in constituting original bispectral images are calculated for fault classification.Experiments on fault diagnosis of gearboxes show that the extracted features can not only reveal some nonlinear characteristics of the system, but also have intuitive meanings with regard to fault characteristic frequencies.These features provide great convenience for the interpretation of the relationships between machinery faults and corresponding bispectra.

References:

[1] Yang Junyan, Zhang Youyun, Zhu Yongsheng.Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension [J].Mechanical Systems and Signal Processing, 2007, 21(5):2012-2024.
[2] Zheng Haibo, Chen Xinzhao, Li Zhiyuan.Bispectrum based gear fault feature extraction and diagnosis [J].Journal of Vibration Engineering, 2002, 15(3):354-358.(in Chinese)
[3] Huang Jinying, Bi Shihua, Pan Hongxia, et al.The research of higher-order cumulant spectrum for vibration signals of gearbox [C]//Proceedings of IEEE International Conference on Information Acquisition. Weihai, China, 2006:1395-1399.
[4] Welling M, Weber M.Positive tensor factorization [J].Pattern Recognition Letters, 2001, 22(12):1255-1261.
[5] Shashua A, Hazan T.Non-negative tensor factorization with applications to statistics and computer vision[C]//Proceedings of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005:793-800.
[6] Fitzgerald D, Cranitch M, Coyle E.Shifted 2D non-negative tensor factorization[C]//Proceedings of IET Irish Signals and Systems Conference. Dublin, Ireland, 2006:509-513.
[7] Park S W, Savvides M.Estimating mixing factors simultaneously in multilinear tensor decomposition for robust face recognition and synthesis[C]//Proceedings of Conference on Computer Vision and Pattern Recognition Workshop. New York, 2006:49-54.
[8] Cichocki A, Zdunek R, Choi S, et al.Non-negative tensor factorization using alpha and beta divergences [C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Honolulu, USA, 2007, 3:1393-1396.
[9] Zhang Qiang, Wang Han, Plemmons R J, et al.Tensor methods for hyperspectral data analysis:a space object material identification study [J].Journal of the Optical Society of America A:Optics and Image Science, and Vision, 2008, 25(12):3001-3012.
[10] Hazan T, Polak S, Shashua A.Sparse image coding using a 3D non-negative tensor factorization [C]//Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005:50-57.
[11] Heiler M, Schnörr C.Controlling sparseness in non-negative tensor factorization[C]//Proceedings of the 9th European Conference on Computer Vision. Graz, Austria, 2006:56-67.
[12] Ding Kang, Li Weihua, Zhu Xiaoyong. Practical technology for fault diagnosis of gears and gearboxes [M].Beijing:China Machine Press, 2005:91-109.(in Chinese)

Memo

Memo:

Biographies: Peng Sen(1986—), male, graduate;Xu Feiyun(corresponding author), male, doctor, professor, fyxu@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.50875048), the Natural Science Foundation of Jiangsu Province(No.BK2007115), the National High Technology Research and Development Program of China(863 Program)(No.2007AA04Z421).
Citation: Peng Sen, Xu Feiyun, Jia Minping, et al.Sparseness-controlled non-negative tensor factorization and its application in machinery fault diagnosis[J].Journal of Southeast University(English Edition), 2009, 25(3):346-350.

Common functions