Abstract
The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
RAJ A S, MURALI N. A novel application of Lucy–Richardson deconvolution: Bearing fault diagnosis [J]. Journal of Vibration and Control, 2015, 21(6): 1055–1067.
LIU Tao, CHEN **, DONG Guang-ming, XIAO Wen-bing, ZHOU Xu-ning. The fault detection and diagnosis in rolling element bearings using frequency band entropy [J]. Journal of Mechanical Engineering Science, 2012, 27(1): 87–99.
LV Yong, ZHU Qing-lin, YUAN Rui. Fault diagnosis of rolling bearing based on fast nonlocal means and envelop spectrum [J]. Sensors, 2015, 15(1): 1182–1198.
MA Jun, WU Jian-de, YUAN Xu-yi. The fault diagnosis of the rolling bearing based on the LMD and time-frequency analysis [J]. International Journal of Control & Automation, 2013, 6(4): 357–376.
YU Ling-li, CAI Zi-xing, ZHOU Zhi, FENG Zhen-qiu. Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filte [J]. Journal of Central South University, 2012, 19: 1249–1257.
LIU Huan-huan, HAN Ming-hong. A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings [J]. Mechanism and Machine Theory, 2014, 75: 67–78.
HE Qing-bo. Time–frequency manifold for nonlinear feature extraction in machinery fault diagnosis [J]. Mechanical Systems and Signal Processing, 2013, 35: 200–218.
ZHENG **-de, CHENG Jun-sheng, YANG Yu. Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis [J]. Mechanical Systems and Signal Processing, 2013, 40: 136–153.
FAN Zhi-qi, LI Huai-zhong. A hybrid approach for fault diagnosis of planetary bearings using an internal vibration sensor [J]. Measurement, 2015, 64: 71–80.
MURUGANATHAM B, SANJITH M A, KRISHNAKUMAR B, SATYAMURTY S A V. Roller element bearing fault diagnosis using singular spectrum analysis [J]. Mechanical Systems and Signal Processing, 2013, 35: 150–166.
WANG Tian-yang, LIANG Ming, LI Jian-yong, CHENG Wei-dong. Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis [J]. Mechanical Systems and Signal Processing, 2014, 45: 139–153.
THOMAS W R, FRANCISCO A B, VAREJÃO F M. Heterogeneous feature models and feature selection applied to bearing fault diagnosis [J]. IEEE Transactions on Industrial Electronics, 2015, 62(1): 637–646.
HARMOUCHE J, DELPHA C, DIALLO D. Improved fault diagnosis of ball bearings based on the global spectrum of vibration signals [J]. IEEE Transactions on Energy Conversion, 2015, 30(1): 376–383.
HECKE B V, QU Y Z, HE D, BECHHOEFER E. A new spectral average-based bearing fault diagnostic approach [J]. J Fail Anal and Preven, 2014, 14: 354–362.
JIANG Rui-hong, LIU Shu-lin, TANG You-fu, LIU Ying-hui. A novel method of fault diagnosis for rolling element bearings based on the accumulated envelope spectrum of the wavelet packet [J]. Journal of Vibration and Control, 2015, 21(8): 1580–1593.
LI Mao-lin, LIANG Lin, WANG Sun-an. Sensitive feature extraction of machine faults based on sparse representation [J]. Journal of Mechanical Engineering, 2013, 49(1): 73–79. (in Chinese)
WANG Guo-biao, HE Zheng-jia, CHEN Xue-feng, LAI Yi-nan. Basic research on machinery fault diagnosis—What is the prescription [J]. Journal of Mechanical Engineering, 2013, 49(1): 63–72. (in Chinese)
ZENG Qing-hu, QIU **g, LIU Guan-jun, TAN **ao-dong. Application of wavelet correlation feature scale entropy to fault diagnosis of roller bearings [J]. Journal of National University of Defense Technology, 2007, 29(6): 102–105. (in Chinese)
WANG Bing-cheng, REN Zhao-hui, WEN Bang-chun. Fault diagnoses method of rotating machines based on nonlinear multiparameters [J]. Journal of Mechanical Engineering, 2012, 48(5): 63–69. (in Chinese)
COIFMAN R, WICKERHAUSER M. Entropy-based algorithms for best basis selection [J]. IEEE Transactions Information Theory, 1992, 38: 1713–1716.
PHILLIPS P J. Matching pursuit filters applied to face identification [J]. IEEE Trans on Image Processing, 1998, 7(8): 1150–1164.
MALLAT S, ZHANG Z. Matching pursuits with time-frequency dictionaries [J]. IEEE Trans on Signal Processing, 1993, 41(12): 3397–3415.
ENGAN K, AASE S O, HAKON-HUSOY J H. Method of optimal directions for frame design [C]// 1999 IEEE Int Conf Acoust, Speech, Signal Process. Phoenix, USA, 1999, 5: 2443–2446.
AHARON M, ELAD M, BRUCKSTEIN A M. K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation [J]. IEEE Trans on Signal Processing, 2006, 54(11): 4311–4322.
Bearing test data. [2015-03]. ttp://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Projects(51375484, 51475463) supported by the National Natural Science Foundation of China; Project(kxk140301) supported by Interdisciplinary Joint Training Project for Doctoral Student of National University of Defense Technology, China
Rights and permissions
About this article
Cite this article
Zhang, Xp., Hu, Nq., Hu, L. et al. A bearing fault diagnosis method based on sparse decomposition theory. J. Cent. South Univ. 23, 1961–1969 (2016). https://doi.org/10.1007/s11771-016-3253-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11771-016-3253-3