Accesso libero

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

INFORMAZIONI SU QUESTO ARTICOLO

Cita

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

R.C.Sr.Eisenmann, “Machinery Malfunction Diagnosis and Correction”, Prentice Hall, 1998. Search in Google Scholar

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 Scholar

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 Scholar

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 Scholar

T.Irvine, “An Introduction to Spectral Functions’’, Vibration Data Press, 1998. Search in Google Scholar

T.Irvine, “Power Spectral Density Units: [G2/Hz]’’, Vibration Data Press, 2000. Search in Google Scholar

C.Cortes and V.Vapnik, “Support-vector network’’, Machine Learning, 20: 1995, pp. 273297.10.1007/BF00994018 Search in Google Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

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 Scholar

eISSN:
1178-5608
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Engineering, Introductions and Overviews, other