Random forests classifier for machine fault diagnosis

Journal of Mechanical Science and Technology

Volumn 22, Issue 9, 2008, Pages 1716-1725

  Yang, Bo Suk ^a   Di, Xiao ^a   Han, Tian ^b  

^a Pukyong National University   (South Korea)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

Author keywords

Fault diagnosis; Genetic algorithm; Machine learning; Random forests algorithm; Rotating machinery

Indexed keywords

APPLICATION RESEARCHES; CLASSIFICATION ACCURACIES; DIAGNOSIS METHODS; ENSEMBLE CLASSIFIERS; EXECUTION SPEED; FAULT DIAGNOSIS; FAULTS DIAGNOSIS; HYBRID METHODS; MACHINE FAULT DIAGNOSIS; MACHINE FAULTS; MACHINE LEARNING; MOTOR FAULTS; RANDOM FORESTS ALGORITHM; TREE CLASSIFIERS;

CLASSIFIERS; DECISION TREES; FAILURE ANALYSIS; GENETIC ALGORITHMS; INDUCTION MOTORS; QUALITY ASSURANCE; ROBOT LEARNING; ROTATING MACHINERY; ROTATION;

LEARNING ALGORITHMS;

EID: 64949202636     PISSN: 1738494X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s12206-008-0603-6     Document Type: Article

References (20)

1. T. G. Dietterich, Ensemble Learning, The Handbook of Brain Theory and Neural Networks, M.A. Arbib (ED.), The MIT Press, Cambridge, MA, (2002).
2. L. Breiman 1996 Bagging Predictors Machine Learning 26 2 123 140
3. E. Bauer R. Kohavi 1999 An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants Machine Learning 36 105 139
4. P. O. Gislason J. A. Benediktsson J. R. Sveinsson 2006 Random Forests for Land Cover Classification Pattern Recognition Letter 27 4 294 300
5. R. E. Schapire 1990 The Strength of Weak Learnability Machine Learning 5 2 197 227
6. S. L. Joelsson, J. A. Benediktsson and J. R. Sveinsson, Random Forest Classifiers for Hyperspectral Data, IEEE Publications, (2005) 160-163.
7. G. J. Briem J. A. Benediktsson J. R. Sveinsson 2002 Multiple Classifiers Applied to Multisource Remote Sensing Data IEEE Trans. Geoscience and Remote Sensing 40 2291 2299
8. L. Breiman 2001 Random Forests Machine Learning 45 1 5 32
9. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, (1986).
10. B. S. Yang C. H. Park H. J. Kim 2000 An Efficient Method of Vibration Diagnostics For Rotating Machinery Using a Decision Tree International Journal of Rotating Machinery 6 1 19 27
11. L. Breiman 2006 Random Forest User Notes Statistics Department, University of California Berkeley L. Breiman, Random Forest User Notes, Statistics Department, University of California, Berkeley, ftp://ftp.stat. berkeley.edu/pub/users/breiman/notes-on-random-forests-v2.pdf, (2006).
12. M. Pal 2005 Random Forest Classifier for Remote Sensing Classification International Journal of Remote Sensing 26 1 217 222
13. K. Remlinger, Introduction and Application of Random Forest on High Throughput Screening Data from Drug Discovery, Proc. of Workshop for the SAMSI Program on Data Mining and Machine Learning, (2003) 1-6.
14. J. S. Ham Y. Chen M. M. Crawford J. Ghosh 2005 Investigation of the Random Forest Framework for Classification of Hyperspectral Data IEEE Trans. Geoscience and Remote Sensing 43 3 492 501
15. X. Di, T. Han and B. S. Yang, Application of Random Forest Algorithm in Machine Fault Diagnosis, Inaugural World Congress on Engineering Asset Management, Gold Coast, Australia, (2006).
16. B. S. Yang T. Han J. L. An 2004 ARTKOHONEN Neural Network for Fault Diagnosis of Rotating Machinery Mechanical Systems and Signal Processing 18 3 645 657
17. L. Breiman, J. H. Friedman, R. A. Olshen and C. J. Stone, Classification and Regression Trees, Wadsworth, Belmont, (1984).
18. G. E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Addison Wesley, New York, (1989).
19. B. S. Yang K. J. Kim 2006 Application of Data Fusion and Dempster-Shafer Theory in Fault Diagnostics of Induction Motors Mechanical Systems and Signal Processing 20 2 403 420
20. A. Widodo T. Han B. S. Yang 2007 Combination of Independent Component Analysis and Multiclass Support Vector Machines for Fault Diagnosis of Induction Motors Expert Systems with Applications 32 2 299 312