Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network

Measurement: Journal of the International Measurement Confederation

Volumn 58, Issue , 2014, Pages 187-196

  Unal, Muhammet ^a   Onat, Mustafa ^a   Demetgul, Mustafa ^a   Kucuk, Haluk ^a  

^a MARMARA UNIVERSITY   (Turkey)

Author keywords

Envelope detector; Fast Fourier Transform artificial neural; Fault diagnostics; Feed forward algorithm; Genetic algorithm; Hilbert Transform; network; Rolling bearing

Indexed keywords

ALGORITHMS; BEARINGS (MACHINE PARTS); COMPLEX NETWORKS; DETECTOR CIRCUITS; FAST FOURIER TRANSFORMS; GENETIC ALGORITHMS; MATHEMATICAL TRANSFORMATIONS; NETWORK ARCHITECTURE; NETWORKS (CIRCUITS); NEURAL NETWORKS; ROLLER BEARINGS; VIBRATION ANALYSIS;

ENVELOPE DETECTORS; FAULT DIAGNOSTICS; FEED-FORWARD; HILBERT TRANSFORM; ROLLING BEARINGS;

FAULT DETECTION;

EID: 84907854703     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2014.08.041     Document Type: Article

References (36)

1. Y. Zhang, and R.B. Randall Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram Mech. Syst. Signal Process. 23 2009 1509 1517
2. N. Tandon, and A. Choudhury A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings Tribol. Int. 32 1999 469 480
3. L. Zhen, H. Zhengjia, Z. Yanyang, and W. Yanxue Customized wavelet denoising using intra-and inter-scale dependency for bearing fault detection J. Sound Vib. 313 2008 342 359
4. R.F. Burchill, Resonant Structure Techniques for Bearing Fault, in: Proceedings of Mechanical Failure Prevention Group, Gaithersburg, 1972.
5. Y. Li, P.W. Tse, and X. Wang Recovery of vibration signal based on a super-exponential algorithm J. Sound Vib. 311 2008 537 553
6. H. Dishan A wavelet-based algorithm for the Hilbert transform Mech. Syst. Signal Process. 10 1996 125 134
7. P.W. Tse, Y.H. Peng, and R. Yam Wavelet analysis and envelope detection for rolling element bearing fault diagnosis: their effectiveness and flexibilities J. Vib. Acoust. 123 2001 303 310
8. J. Altmann, and J. Mathew Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis Mech. Syst. Signal Process. 15 2001 963 977
9. Y. Jiang, B. Tang, Y. Qin, and W. Liu Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD Renewable Energy 36 2011 2146 2153
10. X.L. Wang, Y.X. Zhang, J.P. Zhu, and Z.Q. Shi Research on rolling element bearing fault diagnosis based on singular value decomposition and kurtosis criterion Appl. Mech. Mater. 432 2013 304 309
11. C. Wen, and C.D. Zhou Rolling bearing fault feature extraction based on SVD-EEMD Appl. Mech. Mater. 411 2013 1067 1071
12. D. Yu, J. Cheng, and Y. Yang Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings Mech. Syst. Signal Process. 19 3 2005 259 270
13. Z.K. Peng, P.W. Tse, and F.L. Chu An improved Hilbert-Huang transform and its application in vibration signal analysis J. Sound Vib. 286 2005 187 205
14. V.K. Rai, and A.R. Mohanty Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform Mech. Syst. Signal Process. 21 8 2007 2607 2615
15. Y. Qin, S. Qin, and Y. Mao Research on iterated Hilbert transform and its application in mechanical fault diagnosis Mech. Syst. Signal Process. 22 Nov 2008 1967 1980
16. M. Delgado, G. Cirrincione, A. Garcia, J. Ortega, H. Henao, Accurate bearing faults classification based on statistical-time features, curvilinear component analysis and neural networks, in: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, 2012, pp. 3854-3861.
17. H.M. Ertunc, H. Ocak, and C. Aliustaoglu ANN-and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults Neural Comput. Appl. 2012 1 12
18. E. Germen, M. Basaran, and M. Fidan Sound based induction motor fault diagnosis using Kohonen self-organizing map Mech. Syst. Signal Process. 46 2014 45 58
19. P.K. Kankar, S.C. Sharma, and S.P. Harsha Rolling element bearing fault diagnosis using wavelet transform Neurocomputing 74 5 2011 1638 1645
20. B.S. Yang, T. Han, and J.L. An ART-KOHONEN neural network for fault diagnosis of rotating machinery Mech. Syst. Signal Process. 18 5 2004 645 657
21. R. Hao, Z. Feng, F. Chu, Application of support vector machine based on pattern spectrum entropy in fault diagnostics of bearings, in: Prognostics and Health Management Conference, 2010, PHM'10, 2010, pp. 1-6.
22. R.J. Hao, Z.K. Peng, Z.P. Feng, and F.L. Chu Application of support vector machine based on pattern spectrum entropy in fault diagnostics of rolling element bearings Meas. Sci. Technol. 22 April 2011 045708
23. Z. Shen, N. Yao, H. Dong, and Y. Yao Application of Twin Support Vector Machine for Fault Diagnosis of Rolling Bearing Mechatronics and Automatic Control Systems 2014 Springer 161 167
24. P. Konar, and P. Chattopadhyay Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) Appl. Soft Comput. 11 September 2011 4203 4211
25. S.-D. Wu, P.-H. Wu, C.-W. Wu, J.-J. Ding, and C.-C. Wang Bearing fault diagnosis based on multiscale permutation entropy and support vector machine Entropy 14 Aug 2012 1343 1356
26. V. Sugumaran, and K.I. Ramachandran Effect of number of features on classification of roller bearing faults using SVM and PSVM Expert Syst. Appl. 38 April 2011 4088 4096
27. X. Zhang, X. Chen, and Z. He Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm Proc. Instit. Mech. Eng. Part C: J. Mech. Eng. Sci. 224 2010 217 229
28. M. Demetgul, M. Unal, I.N. Tansel, and O. Yazicioglu Fault diagnosis on bottle filling plant using genetic-based neural network Adv. Eng. Softw. 42 December 2011 1051 1058
29. F.H.F. Leung, H.K. Lam, S.H. Ling, and P.K.S. Tam Tuning of the structure and parameters of a neural network using an improved genetic algorithm Neural Networks, IEEE Trans. 14 2003 79 88
30. A. Saxena, and A. Saad Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems Appl. Soft Comput. 7 January 2007 441 454
31. B. Samanta Gear fault detection using artificial neural networks and support vector machines with genetic algorithms Mech. Syst. Signal Process. 18 May 2004 625 644
32. A. Yiʇit, Detection of Rolling Element Bearing Faults via Vibration Analysis, Yüksek Lisans, Fen Bilimleri Enstitüsü, Dokuz Eylül Üniversitesi, ̄zmir, 2008.
33. M.T. Hagan, H.B. Demuth, and M. Beale Neural Network Design 1997 Thomson Boston
34. M. Ünal, H. Erdal, V. Topuz, Trajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process, Comput. Appl. Eng. Edu. (2010) DOI 10.1002/cae.20420.
35. V. Topuz BulanIk Genetik Proses Kontrolü 2002 Doktora Tezi, Fen Bilimleri Enstitüsü, Marmara Üniversitesi ̄stanbul
36. P.M. Norgaard, The Neural Network Based System Identification Toolbox: For use with MATLAB, 1997.