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An Appropriate Procedure for Detection of Journal-Bearing Fault using Power Spectral Density, K-Nearest Neighbor and Support Vector Machine

International Journal on Smart Sensing and Intelligent Systems
Volume 5 (2012): Issue 3 (January 2012)
By:
Open Access
|Sep 2012

References

  1. [1]B.Samanta, K.R.Al-Balushi and S.A.Al-Araimi, “Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection’’, Engineering Applications of Artificial Intelligence 16, 2003, pp. 657– 665.10.1016/j.engappai.2003.09.006
    Search in Google ScholarBack to article
  2. [2]O.Castro, C.Sisamon and J.Prada, “Bearing fault diagnosis based neural network classification and wavelet transform”, Proc. of the 6th WSEAS International Conference on Wavelet Analysis & Multirate Systems, Bucharest, Romania, 2006, pp. 16-18.
    Search in Google ScholarBack to article
  3. [3]Z.K. Peng, and F. L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography’’, Mechanical Systems and Signal Processing, Vol. 18, 2004, pp. 199–221.10.1016/S0888-3270(03)00075-X
    Search in Google ScholarBack to article
  4. [4]H.Zheng, Z.Li and X.Chen, “Gear fault diagnosis based on continuous wavelet transform’’, Mechanical systems and Signal Processing, Vol. 16 (2–3), 2002, pp. 447–457.10.1006/mssp.2002.1482
    Search in Google ScholarBack to article
  5. [5]K.Mollazade, et.al, “An Intelligent Combined Method Based on Power Spectral Density, Decision Trees and Fuzzy Logic for Hydraulic Pumps Fault Diagnosis’’, International Journal of Electrical and Computer Engineering 3:8, 2008, pp. 551-563.
    Search in Google ScholarBack to article
  6. [6]H.Ahmadi and A.Moosavian, “Fault Diagnosis of Journal-Bearing of Generator Using Power Spectral Density and Fault Probability Distribution Function’’, Communications in Computer and Information Science, 2011, pp. 30–36.10.1007/978-3-642-27337-7_4
    Search in Google ScholarBack to article
  7. [7]P.A.Laggan, “Vibration monitoring”, IEE Proc proceeding Colloquium on Understanding your Condition Monitoring, 1999, pp. 1-11.10.1049/ic:19990653
    Search in Google ScholarBack to article
  8. [8]R.C.Sr.Eisenmann, “Machinery Malfunction Diagnosis and Correction”, Prentice Hall, 1998.
    Search in Google ScholarBack to article
  9. [9]S.Pöyhönen, P.Jover and H.Hyötyniemi, “Independent component analysis of vibration for fault diagnosis of an induction motor”, Proc. on the IASTED International Conference on Circuits, Signals, and Systems (CSS), Mexico, Vol. 1, 2003, pp. 203-208.
    Search in Google ScholarBack to article
  10. [10]L.B.Jack and A.K.Nandi, “Fault detection using support vector machines and artificial neural network augmented by genetic algorithms’’, Mechanical Systems and Signal Processing, Vol 16, 2002, pp. 373-390.10.1006/mssp.2001.1454
    Search in Google ScholarBack to article
  11. [11]R.B.Gibson, “Power Spectral Density: a Fast, Simple Method with Low Core Storage Requirement’’, M.I.T. Charles Stark Draper Laboratory Press, 1972, 57 pages.
    Search in Google ScholarBack to article
  12. [12]T.Irvine, “An Introduction to Spectral Functions’’, Vibration Data Press, 1998.
    Search in Google ScholarBack to article
  13. [13]T.Irvine, “Power Spectral Density Units: [G2/Hz]’’, Vibration Data Press, 2000.
    Search in Google ScholarBack to article
  14. [14]C.Cortes and V.Vapnik, “Support-vector network’’, Machine Learning, 20: 1995, pp. 273297.10.1007/BF00994018
    Search in Google ScholarBack to article
  15. [15]A.Widodo and B.S.Yang, “Review support vector machine in machine condition monitoring and fault diagnosis’’, Mechanical systems and signal processing, 2007, pp. 2560–2574.10.1016/j.ymssp.2006.12.007
    Search in Google ScholarBack to article
  16. [16]Y.Song, et.al, “IKNN: Informative K-nearest neighbor pattern classification”, Proc. Springer-Verlag Berlin Heidelberg, LNAI 4702, 2007, pp. 248–264.10.1007/978-3-540-74976-9_25
    Search in Google ScholarBack to article
  17. [17]A.Widodo and B.S.Yang, “Support vector machine in machine condition monitoring and fault diagnosis’’, Mechanical Systems and Signal Processing, Vol. 21, Issue 6, 2007, pp. 25602574.10.1016/j.ymssp.2006.12.007
    Search in Google ScholarBack to article
  18. [18]Z.Wu, et.al, “Automatic Digital Modulation Recognition Based on Support Vector Machines’’, Neural Networks and Brain, ICNN&B ‘05, Vol. 2, 2005, pp. 1025-1028.
    Search in Google ScholarBack to article
  19. [19]M, Khazaee, et.al, “Fault diagnosis and classification of planetary gearbox of MF285 tractor final drive using Fast Fourier Transform (FFT), Stepwise Backward Selection and support vector machine (SVM) classifier, Elixir Control Engg. 43, 2012, pp. 6974-6977.
    Search in Google ScholarBack to article
  20. [20]K.Heidarbeigi, et.al, “Fault diagnosis of Massey Ferguson gearbox using power spectral density”, Journal of Agriculture Technology 5(1), 2009, pp. 1-6.
    Search in Google ScholarBack to article
  21. [21]J.Cusido, et.al., “Detection in induction machines by using power spectral density on the wavelet decompositions,” MCIA Research Group, Universitaty Politecnica de Catalunya. C. Colom 1, 08222 Terrassa, Spain: Catalunya, 2008, pp. 1-7.
    Search in Google ScholarBack to article
  22. [22]J.Slavic, et.al., “Typical bearing-fault rating using force measurements: application to real data,” Journal of Vibration and Control, 17(14), 2011, pp. 2164-2174.10.1177/1077546311399949
    Search in Google ScholarBack to article
  23. [23]K.M.Saridakis, “Fault Diagnosis of Journal Bearings Based on Artificial Neural Networks and Measurements of Bearing Performance Characteristics”, Proc. of the 9th International Conference on Computational Structures Technology, Civil-Comp Press, Stirlingshire, UK, 2008, Paper 118.
    Search in Google ScholarBack to article
  24. [24]H.Ahmadi and P.Salami, “Using of Power Spectral Density for Condition Monitoring of Fan”, Modern Applied Science, Vol. 4, No. 6, 2010, pp. 54-59.10.5539/mas.v4n6p54
    Search in Google ScholarBack to article
  25. [25]G.Niu, et.al, “Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal”, Expert Systems with Applications 35, 2008, pp. 918–928.10.1016/j.eswa.2007.08.024
    Search in Google ScholarBack to article
  26. [26]B.Bagheri, H.Ahmadi and R.Labbafi, “Implementing discrete wavelet transform and artificial neural networks for acoustic condition monitoring of gearbox”, Elixir Mech. Engg. 35, 2011, pp. 2909-2911.
    Search in Google ScholarBack to article
  27. [27]L.Zhang, G.Xiong, H.Liu, H.Zou and W.Guo, “Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference”, Expert Systems with Applications 37, 2010, pp. 6077-6085.10.1016/j.eswa.2010.02.118
    Search in Google ScholarBack to article
  28. [28]Y.Lei, Z.He, Y.Zi and Q.Hu, “Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs”, Mechanical Systems and Signal Processing, Vol. 21, Issue. 5, 2007, pp. 2280-2294.10.1016/j.ymssp.2006.11.003
    Search in Google ScholarBack to article
  29. [29]B.S.Yang, T.Han and W.W.Hwang, “Fault diagnosis of rotating machinery based on multiclass support vector machine”, Journal of Mechanical Science and Technology 19(3), 2005, pp. 846-859.10.1007/BF02916133
    Search in Google ScholarBack to article
  30. [30]E.Ebrahimi and K.Mollazade, “Intelligent fault classification of a tractor starter motor using vibration monitoring and adaptive neuro-fuzzy inference system”, Insight, Vol. 52, No. 10, 2010, pp. 561-566.10.1784/insi.2010.52.10.561
    Search in Google ScholarBack to article
  31. [31]S.G.Jolandan, H.Mobli, H.Ahmadi, M.Omid and S.S.Mohtasebi, “Fuzzy-Rule-Based faults classification of gearbox tractor”, WSEAS Transactions on Applied and Theoretical Mechanics, Vol. 7, Issue. 2, 2012, pp. 69-82.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.21307/ijssis-2017-502 | Journal eISSN: 1178-5608
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Language: English
Page range: 685 - 700
Accepted on: Aug 5, 2012
Published on: Sep 1, 2012
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
condition monitoring,
power spectral density,
k-nearest neighbor,
support vector machine,
vibration signal,
fault diagnosis
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2012 A. Moosavian, H. Ahmadi, A. Tabatabaeefar, B. Sakhaei, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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