Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks

Neurocomputing

Volumn 74, Issue 17, 2011, Pages 2941-2952

  Zhang, Kui ^a   Li, Yuhua ^b   Scarf, Philip ^a   Ball, Andrew ^c  

^a UNIVERSITY OF SALFORD   (United Kingdom)

^b UNIVERSITY OF ULSTER   (United Kingdom)

^c UNIVERSITY OF HUDDERSFIELD   (United Kingdom)

Author keywords

Binary search; Fault diagnosis; Feature selection; Radial basis function networks; Sequential backward search

Indexed keywords

BINARY SEARCH; CHI-SQUARED; CLASS SEPARABILITY; CLASSIFICATION METHODS; CURSE OF DIMENSIONALITY; DATA VARIANCE; FILTER MODEL; FISHER SCORE; FREQUENCY FEATURES; GAIN RATIO; HIGH-DIMENSIONAL; HYBRID MODEL; INFORMATION GAIN; INPUT FEATURES; MACHINERY FAULTS; MULTIPLE FEATURES; MULTIPLE MODELS; PEARSON CORRELATION COEFFICIENTS; RADIAL BASIS FUNCTION CLASSIFICATIONS; RADIAL BASIS FUNCTIONS; RELIEF ALGORITHM; SEQUENTIAL BACKWARD SEARCH; VARIABLE RANKING; WEIGHTED VOTING; WRAPPER MODEL;

CORRELATION METHODS; FAILURE ANALYSIS; FEATURE EXTRACTION; MACHINERY;

RADIAL BASIS FUNCTION NETWORKS;

ARTICLE; BINARY SEARCH MODEL; CHI SQUARE TEST; CLASS SEPARABILITY; CLASSIFICATION; CORRELATION COEFFICIENT; DATA VARIANCE; FEATURE SELECTION MODEL; FISHER SCORE; GAIN RATIO; INFORMATION GAIN; INFORMATION PROCESSING; MACHINE LEARNING; MACHINERY FAULT DIAGNOSIS; MATHEMATICAL MODEL; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RADIAL BASED FUNCTION; RELIEF ALGORITHM; SEQUENTIAL BACKWARD EARCH MODEL; STATISTICAL PARAMETERS;

EID: 80052944609     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.03.043     Document Type: Article

References (58)

1. Isermann R. Supervision, fault-detection and fault-diagnosis methods-an introduction. Control Engineering Practice 1997, 5:639-652.
2. Lai W., Tse P.W., Zhang G., Shi T. Classification of gear faults using cumulants and the radial basis function network. Mechanical Systems and Signal Processing 2004, 18:381-389.
3. Li Y., Pont M.J., Jones N.B. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where 'unknown' faults may occur. Pattern Recognition Letters 2002, 23:569-577.
4. Yuan S., Chu F. Support vector machines-based fault diagnosis for turbo-pump rotor. Mechanical Systems and Signal Processing 2006, 20:939-952.
5. Samanta B., Al-Balushi K.R., Al-Araimi S.A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Application of Artificial Intelligence 2003, 16:657-665.
6. Mechefske C.K., Mathew J. Fault detection and diagnosis in low speed rolling element bearings part II: the use of nearest neighbour classification. Mechanical Systems and Signal Processing 1992, 6:309-316.
7. D. Koller, M. Sahami, Year. Toward optimal feature selection, in: Proceedings of International Conference on Machine Learning, 1996, pp. 284-292.
8. Baydar N., Chen Q., Ball A.D., Kruger U. Detection of incipient tooth defect in helical gears using multivariate statistics. Journal of Mechanical Systems and Signal Processing 2001, 15:303-321.
9. He Q., Kong F., Yan R. Subspace-based gearbox condition monitoring by kernel principal component analysis. Mechanical Systems and Signal Processing 2007, 21:1755-1772.
10. Trendafilova I. An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition. Mechanical Systems and Signal Processing 2010, 24:1858-1869.
11. Hernandez F., Caveda O., Atxa V., Altuna J. Application of higher-order statistics on rolling element bearings diagnosis. Innovative Algorithms and Techniques in Automation, Industrial Electronics and Telecommunications 2007, Springer. T. Sobh, K. Elleithy, A. Mahmood, M. Karim (Eds.).
12. W. Li, A Study of Diesel Engine Acoustic Characteristics, Ph. D. Thesis, University of Manchester, 2000.
13. Li W., Gu F., Ball A.D., Leung A.Y.T., Phipps C.E. A study of the noise from diesel engines using independent component analysis. Mechanical Systems and Signal Processing 2001, 15:1165-1184.
14. Li Z., He Y., Chu F., Han J., Hao W. Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model. Journal of Sound and Vibration 2006, 291:60-71.
15. Sugumaran V., Muralidharan V., Ramachandran K.I. Feature selection using decision tree and classification through proximal support vector machine for fault diagnosis of roller bearing. Mechanical Systems and Signal Processing 2007, 21:930-942.
16. Casimir R., Boutleux E., Clerc G., Yahoui A. The use of feature selection and nearest neighbors rule for faults diagnostic in induction motors. Engineering Application of Artificial Interlligence 2006, 19:169-177.
17. Lezanski P. An intelligent system for grinding wheel condition monitoring. Journal of Materials Processing Technology 2001, 109:258-263.
18. Varghese K.C., Williams J.H., Towill D.R. Computer aided feature selection for enhanced analogue system fault location. Pattern Recognition 1978, 10:265-280.
19. Widodo A., Yang B.-S., Han T. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Application 2007, 32:299-312.
20. Jack L.B., Nandi A.K. Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechinical Systems and Signal Processing 2002, 16:373-390.
21. Yuan S.-F., Chu F.-L. Fault diagnosis based on particle swarm optimisation and support vector machines. Mechinical Systems and Signal Processing 2007, 21:1787-1798.
22. Jain A., Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19:153-158.
23. Kohavi R., John G.H. Wrappers for feature subset selection. Artificial Intelligence 1997, 97:273-324.
24. Bishop C.M. Neural Networks for Pattern Recognition 1997, Oxford University Press, Oxford.
25. J. Doak, Intrusion Detection: The Application of Input selection, a Comparison of Algorithm, and the Application of a Wide Area Network Analyzer, Master Thesis, University of California, 1992.
26. Uncu O., Turksen I.B. A novel feature selection approach: combining feature wrappers and filters. Information Sciences 2007, 177:449-466.
27. Narendra P.M., Fukunaga K. A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers 1977, C-26:917-922.
28. Srinivas M., Patnaik L.M. Genetic algorithms: a survey. Computer 1994, 17-26.
29. Kudo M., Sklansky J. Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 2000, 33:25-41.
30. Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 2005, 17:491-502.
31. Dash M., Liu H. Feature selection for classification. Intelligent Data Analysis 1997, 1:131-156.
32. Guyon I., Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research 2003, 3:1157-1182.
33. Rice J.A. Mathematical Statistics and Data Analysis 2007.
34. Aha D.W., Kibler D., Albert M.K. Instance-based learning algorithms. Machine Learning 1991, 6:37-66.
35. Chen Y.-W., Lin C.-J. Combining SVMs with various feature selection strategies. Studies in Fuzziness and Soft Computing 2006, Springer, Berlin/Heidelberg.
36. F.L. Chen, F.C. Li, Combination of feature selection approaches with SVM in credit scoring, Expert Systems with Applications, 2009.
37. Liu Y., Zheng Y.F. FS_SFS: a novel feature selection method for support vector machines. Pattern Recognition 2006, 39:1333-1345.
38. Hoel P.G. Elementary Statistics 1976, Wiley, New York.
39. Banks S., Meier N.C., Burke C.J. Laboratory tests of sampling techniques: comment and rejoinders. Public Opinion Quarterly 1947, 12:316-324.
40. Hoel P.G. Introduction to Mathematical Statistics 1972, John Wiley and Sons, Inc., New York.
41. Mitchell T.M. Machine Learning 1997, The Mc-Graw-Hill Companies, Inc.
42. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2001, John Wiley and Sons, New York.
43. Li K., Jiang D., Xiong K., Ding Y. Application of RBF and SOFM Neural Networks on Vibration Fault Diagnosis for Aero-engine 2006, Springer, Berlin/Heidelberg.
44. Li Y., Pont M.J., Jones N.B., Twiddle J.A. Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems. Transactions of the Institute of Measurement and Control 2001, 23:315-343.
45. R.N. Mahanty, P.B.D. Gupta, Application of RBF neural network to fault classification and location in transmission lines, in: IEE Proceedings of Generation, Transmission and Distribution, vol. 151, 2004, pp. 201-212.
46. Liu C., Jiang D., Zhao M. Application of RBF and Elman Neural Networks on Condition Prediction in CBM 2009, Springer, Berlin/Heidelberg.
47. Meireles M.R.G., Almeida P.E.M., Simões M.G. A comprehensive review for industrial applicability of artificial neural networks. IEEE Transactions on Industrial Electronics 2003, 50:585-601.
48. Garg S., Patra K., Pal S.K., Chakraborty D. Effect of different basis functions on a radial basis function network in prediction of drill flank wear from motor current signals. Soft Computing-A Fusion of Foundations, Methodologies and Applications 2008, 12:777-787.
49. D. Brezak, T. Udiljak, K. Mihoci, D. Majetic, B. Novakovic, J. Kasac, Year. Tool wear monitoring using radial basis function neural network, in: Proceedings of 2004 IEEE International Joint Conference on Neural Networks, 2004, pp. 1859-1862.
50. J.-W. Park, R.G. Harley, G.K. Venayagamoorthy, Year. Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator, in: Proceedings of IEEE Power Engineering Society Winter Meeting, New York, 2002, pp. 274-279.
51. Girosi F., Poggio F. Networks and the best approximation property. Biological Cybernetics 1990, 63:169-176.
52. Bishop C.M. Neural networks and their applications. Review of Scientific Instruments 1994, 65:1803.
53. Zhang K., Li Y., Fan Y., Gu F., Ball A. An evaluation of the potential offered by a relevance vector classifier in machinery fault diagnosis. International Journal of COMADEM 2006, 9:35-40.
54. I.M. Howard, A review of rolling element bearing vibration "etection, diagnosis and prognosis";, DSTO Research Report 0013, 1994.
55. Thorsen O.V., Dalva M. A survey of faults on induction motors in offshore oil industry, petrochemical industry, gas terminals, and oil refineries. IEEE Transactions on Industry Applications 1995, 31:1186-1196.
56. Penman J., Sedding H.G., Lloyd B.A., Fink W.T. Detection and location of interturn short circuits in the stator windings of operating motors. IEEE Transactions on Energy Conversion 1994, 9:652-658.
57. Bonnett A.H., Soukup G.C. Rotor failures in squirrel cage induction motors. IEEE Transactions Industry Application 1986, IA-22:1165-1173.
58. Thomson W.T., Fenger M. Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine 2001, 7:26-34.