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Chinese Journal of Mechanical Engineering

, Volume 28, Issue 1, pp 96–105 | Cite as

Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization

  • Huizhong Gao
  • Lin Liang
  • Xiaoguang Chen
  • Guanghua Xu
Article

Abstract

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, the time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution(TFD) with non-negative matrix factorization(NMF), and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform(STFT). Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network(ANN), NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

Keywords

time-frequency distribution non-negative matrix factorization rolling element bearing feature extraction 

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References

  1. [1]
    CHEN Xiaoguang, LIANG Lin, XU Guanghua, et al. Feature extraction of kernel regress reconstruction for fault diagnosis based on self-organizing manifold learning[J]. Chinese Journal of Mechanical Engineering, 2013, 26(5): 1041–1049.CrossRefGoogle Scholar
  2. [2]
    SMAMANTA B, NATARAJ C. Application of particle swarm optimization and proximal support vector machines for fault detection[J]. Swarm Intelligence, 2009, 3(4): 303–325.CrossRefGoogle Scholar
  3. [3]
    LEI Yaguo, HE Zhengjia, ZI Yanyang, et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs[J]. Mechanical Systems and Signal Processing, 2007, 21(5): 2280–2294.CrossRefGoogle Scholar
  4. [4]
    SUGUMARAN V, RAMACHANDRAN K I. Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing[J]. Mechanical Systems and Signal Processing, 2007, 21(5): 2237–2247.CrossRefGoogle Scholar
  5. [5]
    HE Qingbo, WANG Xiangxiang, ZHOU Qiang. Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis[J]. Sensors, 2014, 14(1): 382–402.CrossRefGoogle Scholar
  6. [6]
    LU Feng, FENG Fuzhou. Wavelet transform technology in the non-stationary signal fault diagnosis in engineering application[J]. Advanced Materials Research, 2012, 518(1): 1355–1358.CrossRefGoogle Scholar
  7. [7]
    WANG Huaqing, CHEN Peng. Fuzzy diagnosis method for rotating machinery in variable rotating speed[J]. Sensors Journal, 2011, 11(1): 23–34.CrossRefGoogle Scholar
  8. [8]
    ZHOU Changjun, WANG Lan, ZHANG Qiang, et al. Face recognition based on PCA image reconstruction and LDA[J]. Optik, 2013, 124(22): 5599–5603.CrossRefGoogle Scholar
  9. [9]
    DU Xianfeng, LI Zhijun, BI Fengrong, et al. Source separation of diesel engine vibration based on the empirical mode decomposition and independent component analysis[J]. Chinese Journal of Mechanical Engineering, 2012, 25(3): 557–563.CrossRefGoogle Scholar
  10. [10]
    LIU Hongxing, LI Jian, ZHAO Ying, et al. Improved singular value decomposition technique for detection and extraction periodic impulse component in a vibration signal[J]. Chinese Journal of Mechanical Engineering, 2004, 17(3): 340–345.CrossRefMathSciNetGoogle Scholar
  11. [11]
    LI Weihua, SHI Tielin, LIAO Guanglan, et al. Feature extraction and classification of gear faults using principal component analysis[J]. Journal of Quality in Maintenance Engineering, 2003, 9(2): 132–143.CrossRefGoogle Scholar
  12. [12]
    LIANG Xingyu, WANG Yuesen, SHU Genqun, et al. Identification of axial vibration excitation source in vehicle engine crankshafts using an auto-regressive and moving average model[J]. Chinese Journal of Mechanical Engineering, 2011, 24(6): 1022–1027.CrossRefGoogle Scholar
  13. [13]
    LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788–791.CrossRefGoogle Scholar
  14. [14]
    LIU Weixiang, ZHENG Nanning. Non-negative matrix factorization based methods for object recognition[J]. Pattern Recognition Letters, 2004, 25(8): 893–897.CrossRefGoogle Scholar
  15. [15]
    PU Xiaorong, ZHANG Yi, ZHENG Zinming, et al. Face recognition using fisher non-negative matrix factorization with sparseness constraints[J]. Lecture Notes in Computer Science, 2005, 3497: 112–117.CrossRefGoogle Scholar
  16. [16]
    LIU Haifeng, WU Zhaohui, CAI Deng, et al. Constrained non-negative matrix factorization for image representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1299–1311.CrossRefGoogle Scholar
  17. [17]
    MEHMOOD A, DAMARLA T, SABATIER J. Separation of human and animal seismic signatures using non-negative matrix factorization[J]. Pattern Recognition Letters, 2012, 33(16): 2085–2093.CrossRefGoogle Scholar
  18. [18]
    LI Bing, ZHANG Peilin, LIU Dongsheng, et al. Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization[J]. Journal of Sound and Vibration, 2011, 330(10): 2388–2399.CrossRefGoogle Scholar
  19. [19]
    WANG Qinghua, ZHANG Youyun, CAI Lei, et al. Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble[J]. Mechanical Systems and Signal Processing, 2009, 23(5): 1683–1695.CrossRefGoogle Scholar
  20. [20]
    YOO J, CHOI S. Orthogonal non-negative matrix tri-factorization for co-clustering: multiplicative updates on siefel manifolds[J]. Information Processing and Management, 2010, 46(5): 559–570.CrossRefGoogle Scholar
  21. [21]
    PAATERO P, TAPPER U. Positive matrix factorization: a nonnegative factor model with optimal utilization of error estimates of data values[J]. Environmetrics, 1994, 5(2): 111–126.CrossRefGoogle Scholar
  22. [22]
    DING C, LI Tao, PENG Wei, et al. Orthogonal nonnegative matrix tri-factorizations for clustering[C]//Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, USA, August 20–23, 2006: 126–135.Google Scholar
  23. [23]
    LI Tao, DING C. The relationships among various nonnegative matrix factorization methods for clustering[C]//Proceedings of the 6th International Conference on Data Mining, Hong Kong, China, December 18–22, 2006: 362–371.Google Scholar
  24. [24]
    OKUN O G. Non-negative matrix factorization and classifiers: experimental study[C]//Proceedings of the 4th IASTED International Conference on Visualization, Imaging, and Image Processing, Marbella, Spain, September 6–8, 2004: 550–555.Google Scholar
  25. [25]
    KIM J, PARK H. Sparse Nonnegative Matrix Factorization for Clustering[R]. Georgia, Atlanta, Georgia Institute of Technology, 2008.Google Scholar
  26. [26]
    DING C, LI Tao, PENG Wei. On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing[J]. Computational Statistics & Data Analysis, 2008, 52(8): 3913–3927.CrossRefzbMATHMathSciNetGoogle Scholar
  27. [27]
    LIU Haining, LIU Chengliang, HUANG Yixiang. Adaptive feature extraction using sparse coding for machinery fault diagnosis[J]. Mechanical Systems and Signal Processing, 2011, 25(2): 558–574.CrossRefGoogle Scholar
  28. [28]
    CICHOCKI A, ZDUNEK R, PHAN A H, et al. Nonnegative matrix and tensor factorizations[M]. West Sussex: John Wiley & Sons Inc, 2009.Google Scholar

Copyright information

© Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Huizhong Gao
    • 1
    • 4
  • Lin Liang
    • 1
    • 2
  • Xiaoguang Chen
    • 1
  • Guanghua Xu
    • 1
    • 3
  1. 1.School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.Key Laboratory for Modern Design and Rotor-Bearing System of Education MinistryXi’an Jiaotong UniversityXi’anChina
  3. 3.State Key Laboratory for Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anChina
  4. 4.The 705 Research InstituteChina Shipbuilding Industry CorporationXi’anChina

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