AN APPROPRIATE PROCEDURE FOR DETECTION OF JOURNAL-BEARING FAULT USING POWER SPECTRAL DENSITY, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

7
Reader(s)
24
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 5 , ISSUE 3 (September 2012) > List of articles

AN APPROPRIATE PROCEDURE FOR DETECTION OF JOURNAL-BEARING FAULT USING POWER SPECTRAL DENSITY, K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE

A. Moosavian * / H. Ahmadi / A. Tabatabaeefar / B. Sakhaei

Keywords : condition monitoring, power spectral density, k-nearest neighbor, support vector machine, vibration signal, fault diagnosis

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 5, Issue 3, Pages 685-700, DOI: https://doi.org/10.21307/ijssis-2017-502

License : (CC BY-NC-ND 4.0)

Received Date : 20-June-2012 / Accepted: 05-August-2012 / Published Online: 01-September-2012

ARTICLE

ABSTRACT

Journal-bearings play a significant role in industrial applications and the necessity of condition monitoring with nondestructive tests is increasing. This paper deals a proper fault detection technique based on power spectral density (PSD) of vibration signals in combination with K-Nearest Neighbor and Support Vector Machine (SVM). The frequency domain vibration signals of an internal combustion engine with three journal-bearing conditions were gained, corresponding to, (i) normal, (ii) corrosion and (iii) excessive wear. The features of the PSD values of vibration signals were extracted using statistical and vibration parameters. The extracted features were used as inputs to the KNN and SVM for three-class identification. The roles of PSD technique and the KNN and SVM classifiers were investigated. Results showed that the accuracy rate of fault diagnosis was 100%. Also, the results demonstrated that the combined PSD-SVM model had the potential for fault diagnosis of engine journal-bearing.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[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.
[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.
[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.
[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.
[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.
[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.
[7] P.A.Laggan, "Vibration monitoring", IEE Proc proceeding Colloquium on Understanding your Condition Monitoring, 1999, pp. 1-11.
[8] R.C.Sr.Eisenmann, “Machinery Malfunction Diagnosis and Correction”, Prentice Hall, 1998.
[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.
[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.
[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.
[12] T.Irvine, “An Introduction to Spectral Functions’’, Vibration Data Press, 1998.
[13] T.Irvine, “Power Spectral Density Units: [G2/Hz]’’, Vibration Data Press, 2000.
[14] C.Cortes and V.Vapnik, “Support-vector network’’, Machine Learning, 20: 1995, pp. 273-297.
[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.
[16] Y.Song, et.al, "IKNN: Informative K-nearest neighbor pattern classification", Proc. Springer-Verlag Berlin Heidelberg, LNAI 4702, 2007, pp. 248–264.
[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. 2560-2574.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[29] B.S.Yang, T.Han and W.W.Hwang, “Fault diagnosis of rotating machinery based on multi-class support vector machine”, Journal of Mechanical Science and Technology 19(3), 2005, pp. 846-859.
[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.
[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.

EXTRA FILES

COMMENTS