Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery

Applied Soft Computing Journal

Volumn 8, Issue 4, 2008, Pages 1365-1380

  Zio, E ^a   Baraldi, P ^a   Gola, G ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Ensemble; Fault diagnosis; Feature selection; Multi objective genetic algorithms; Rotating machinery

Indexed keywords

CLASSIFIERS; FEATURE EXTRACTION; FORECASTING; GENETIC ALGORITHMS; HEALTH; LEARNING SYSTEMS; ROTATING MACHINERY; ROTATION;

BASE CLASSIFIERS; CLASSIFICATION PERFORMANCE; DIAGNOSTIC MODELING; ENSEMBLE; ENSEMBLE OF CLASSIFIERS; FAULT CLASSIFICATION; FAULT DIAGNOSIS; FEATURE SELECTION; FEATURE SUBSETS; FEATURE-BASED CLASSIFIER; MULTI-OBJECTIVE GENETIC ALGORITHM; MULTI-OBJECTIVE GENETIC ALGORITHMS; MULTIPLE FAULTS; NUMERICAL EXPERIMENTS; OBJECTIVE FUNCTION; PATTERN SPACE; SUB-REGIONS;

CLASSIFICATION (OF INFORMATION);

EID: 50149090206     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.10.005     Document Type: Article

References (52)

1. Ragulskis K., and Yurkauskas A. Vibration of Bearings (1989), Hemisphere, Bristol, PA
2. Lipovszky G., Solyomvari K., and Varga G. Vibration Testing of Machines and Their Maintenance (1990), Elsevier, Amsterdam, The Netherlands
3. House J.M., Lee W.Y., and Shin D.R. Classification techniques for fault detection and diagnosis of an air-handling unit. ASHRAE Trans. 105 1 (1999) 1987-1997
4. Robinson J.C., Canada R.G., and Piety K.R. PeakVue analysis-new methodology for bearing fault detection. Sound Vib. 30 11 (1996) 22-25
5. Korablev S., Shapin V., and Filatov Y. Vibration Diagnostics in Precision Instruments (1989), Hemisphere, Bristol, PA
6. Agarwal J., Blockley D., and Woodman N. Vulnerability of structural systems. Struct. Saf. J. 25 3 (2003)
7. Donley M., Stokes W., Jeong G.S., Suh K.K., and Jung S.G. Validation of finite element models for noise/vibration/harshness simulation. Sound Vib. 30 8 (1996) 18-23
8. Chow M.-Y., and Yee S.-O. An adaptive back-propagation through time training algorithm for a neural controller. Proc. IEEE Int. Symp. Intell. Control (1991) 170-175
9. Chow M.-Y., Lee Y.-S., and Trussell H.J. Motor incipient fault detection using artificial neural network and fuzzy logic technologies. Computer Aided Maintenance. Methodology and Practices (1996), Chapman and Hall, London, UK
10. Li B., Chow M.-Y., Tipsuwan Y., and Hung J.C. Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 47 5 (2000)
11. E. Zio, G. Gola, 2007, A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery, Reliabil. Eng. Syst. Saf. (RESS), in press.
12. Tsymbal A., Puuronen S., and Skrypnyk I. Ensemble feature selection with dynamic integration of classifiers. International ICSC Congress on Computational Intelligence Methods (2001)
13. Tsymbal A., Pechenizkiy M., and Cunningham P. Diversity in search strategies for ensemble feature selection. Inf. Fus. (2005)
14. Dietterich T.G. Ensemble Methods in Machine Learning, Lecture Notes in Computer Science (2000), Springer
15. Chandra A., and Yao X. Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69 (2006) 686-700
16. Zhou Z.-H., Jiang Y., Yang Y.-B., and Chen S.-F. Lung cancer cell identification based on artificial neural network ensembles. Artif. Intell. Med. 24 1 (2002)
17. Holland J.H. Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control and Artificial Intelligence. 4th ed. (1975), MIT Press
18. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley Publ. Co.
19. Chambers L. Practical Handbook of Genetic Algorithms: Applications (vol. I); Newfrontiers (vol. II) (1995), CRC Press
20. Sawaragy Y., Nakayama H., and Tanino T. Theory of Multi-Objective Optimization (1985), Academic Press, Orlando, Florida
21. Raymer M.L., Punch W.F., Goodman E.D., Khun L.A., and Jain A.K. Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput. 4 2 (2000)
22. Bozdogan H. Statistical data mining with informational complexity and genetic algorithm. In: Bozdogan H. (Ed). Statistical Data Mining and Knowledge Discovery (2003), CRC Press 29
23. Chipperfield A.J., Fonseca C.M., and Fleming P.J. Development of genetic optimization tools for multi-objective optimization problems in CACSD. IEE Colloquium on Genetic Algorithms for Control Systems Engineering. The Institution of Electrical Engineers. Digest No. 1992/106 (1992) 3/1-3/6
24. Fonseca C.M., and Fleming P.J. Multi-objective genetic algorithms. IEE Colloquium on Genetic Algorithms for Control Systems Engineering (2002) 6/1-6/5
25. Yang J., and Honavar V. Feature subset selection using a genetic algorithm. IEEE Intell. Syst. Appl. 13 2 (1998) 44-49
26. Oliveira L.S., Sabourin R., Bortolozzi F., and Suen C.Y. Feature selection using multi-objective genetic algorithm for handwritten digit recognition. Proceedings of 16th International Conference on Pattern Recognition, vol. 1 (2002) 568-571
27. Murata T., and Ishibuchi H. MOGA: multi-objective genetic algorithms. IEEE International Conference on Evolutionary Computation, vol. 1 (1995) 289
28. Brown G., Yao X., Wyatt J., Wersing H., and Sendhoff B. Exploiting ensemble diversity for automatic feature extraction. Proceedings of the 9th International Conference on Neural Information Processing (2002)
29. L.I. Kuncheva, C.J. Whitaker, Ten measures of diversity in classifier ensembles: limits for two classifiers, A DERA/IEE Workshop on Intelligent Sensor Processing (Ref. No. 2001/050), 2001, pp. 10/1-10/10.
30. Brown G., Wyatt J., Harris R., and Yao X. Diversity creation methods: a survey and categorisation 6 1 (2005) 5-20
31. Kilundu B., and Dehombreux P. A feature selection approach based on principal component analysis and synaptic weights for mechanical fault diagnosis. In: Kolowrocki (Ed). Advances in Safety and Reliability (2005), Taylor and Francis Group, London, UK
32. Tumer K., and Ghosh J. Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recog. 29 2 (1996) 341-348
33. Krogh A., and Vedelsby J. Neural network ensembles, cross validation, and active learning. Neural Inf. Process. Syst. 7 (1995) 231-238
34. Opitz D., and Maclin R. Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11 (1999) 169-198
35. Opitz D.W., and Shavlik J.W. Generating accurate and diverse members of a neural-network ensemble. Neural Inf. Process. Syst. 8 (1996) 535-541
36. Dietterich T.G. Machine-learning research: four current directions. AI Mag. 18 4 (1998) 97-136
37. Hansen L.K., and Salamon P. Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12 10 (1990) 993-1001
38. Valentini G., and Masulli F. Ensembles of learning machines. In: Tagliaferri R., and Marinaro M. (Eds). Neural Nets WIRN Vietri-2002 (LNCS 2486) (2002), Springer-Verlag 3-19
39. Abbass H.A. Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization. The IEEE 2003 Conference on Evolutionary Computation, vol. 3 (2003), IEEE Press 2074-2080
40. A. Chandra, X. Yao, Multi-Objective Ensemble Construction, Learning and Evolution, available at http://dbkgroup.org/knowles/MPSN3/mpsn3chandra.pdf.
41. Zio E., Baraldi P., and Pedroni N. Selecting features for nuclear transients classification by means of genetic algorithms. IEEE Trans. Nucl. Sci. 53 3 (Part 3) (2006) 1479-1493
42. Keller J.M., Gray M.R., and Givens J.A. A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. SMC-15 4 (1985) 580-585
43. Marcelloni F. Feature selection based on a modified fuzzy c-means algorithm with supervision. Inf. Sci. 151 (2003) 201-256
44. A. Chandra, Evolutionary approach to tackling the trade-off between diversity and accuracy in neural network ensembles, Technical report, School of Computer Science, The University of Birmingham, UK, April 2004.
45. Chandra A., and Yao X. DIVACE: diverse and accurate ensemble learning algorithm. Proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning (LNCS 3177) (2004), Springer-Verlag, Exeter, UK 619-625
46. Zio E., Baraldi P., and Pedroni N. Feature selection for transients classification by a niched pareto genetic algorithm. In: Ruan D., D'Hondt P., Fantoni P., Cock M.D., Nachtegael M., and Kerre E.E. (Eds). Applied Artificial Intelligence: Proceeding of the 7th International Flins Conference (2006) 938-945
47. Fonseca P.M., and Fleming P.J. An overview of evolutionary algorithms in multiobjective optimization. Evol. Comput. 3 1 (1995) 1-16
48. Jakob W.D., Gorges-Schleuter M., and Blume C. Application of genetic algorithms to task planning and learning. In: Männer R., and Manderick B. (Eds). Parallel Problem Solving from Nature vol. 2 (1992), North-Holland, Amsterdam, The Netherlands 291-300
49. Ishibuchi H., and Murata T. A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. Syst. Man Cybern. Part C: Appl. ev. 28 3 (1998)
50. Schaffer J.D. Multiple objective optimization with vector evaluated genetic algorithms. In: Grefenstette J. (Ed). Proceedings of the International Conference on Genetic Algorithms and their Applications (1985) 93-100
51. E. Zitzler, Evolutionary algorithms for multiobjective optimization: methods and applications, PhD Dissertation, Swiss Federal Inst. Technol., Zurich, 1999.
52. Zitzler E., Laumanns M., Thiele L., Fonseca C.M., and da Fonseca V.G. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7 2 (2003) 117-132