Browse > Article
http://dx.doi.org/10.5050/KSNVN.2004.14.12.1233

Fault Diagnosis of Rotating Machinery Using Multi-class Support Vector Machines  

Hwang, Won-Woo (부경대학교 대학원 기계공학부)
Yang, Bo-Suk (부경대학교 기계공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.14, no.12, 2004 , pp. 1233-1240 More about this Journal
Abstract
Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.
Keywords
Fault Diagnosis; Kernel Function; Support Vector Machine(SVM); Hotating Machinery; Vibration Signal;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Williams, J.H., Davies A. and Drakes, P.R.. 'Condition-based Maintenance and Machine Diagnostics,' Chapman & Hall, 1994
2 Lim, D. S., Yang, B. S. and Kim, D.J.. 2000. 'An Expert System for Vibration Diagnosis of Rotating Machinery Using Decision Trees,' International Journal of Condition Monitoring and Diagnostic Engineering. Management (COMADEM), Vol.3, No.4, pp. 31-36
3 백두진, 이용복, 김승종, 김창호, 장건희, 2003,'산업용 터보기기 결함진단을 위한 복합적 데이터베이스 구조의 퍼지전문가시스템,' 한국소음진동공학회논문집, 제13권, 제9호,pp.703-712
4 Jack. L.B. and Nandi, A.K., 2002, 'Fault Detection using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms.' Mechanical Sy stems and Signal Processing, Vol. 16, No.2-3, pp. 373-390   DOI   ScienceOn
5 황원우, 고명환, 양보석, 2004, 'SVM을 이용한 버터플라이밸브의 캐비테이션 상태감시,' 한국소음진동공학회논문집, 제14권 제2호, PP.119-127
6 Yang, B.S., Hwang, W. W., Kim, D.J. and Tan. A. C., 2005, 'Condition Classification of Small Reciprocating Compressor for Refrigerators using Artificial Neural Networks and Support Vector Machines,' Mechanical Systems and Signal Processing, Vol. 19, pp. 371-390   DOI   ScienceOn
7 M.A. Hearst. B. Scholkopf. S. Dumais. E. Osuna. J. Platt. 1998, 'Trends and Controversies-Support Vector Machines,' IEEE Intelligent System, Vol.13. No.4, pp. 18-28   DOI   ScienceOn
8 N. Cristianini, J. S. Taylor. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press
9 Platt, J., 1999, Fast Training of Support Vector Machines Using Sequential Minimal Optimization, In B. Scholkopf, C. J. C. Burges, A. J. Smola, Advances in Kernel Methods-Support Vector Learning. MIT Press, pp. 336-342
10 Keerthi, S. S. and Shevade, S.K., 2002. 'SMO Algorithm for Least Squares SVM Formulations,' Control Di vision Technical Report CD-02-8
11 KreBel, U.. 1999. Pairwise Classification and Support Vector Machines, in Advances in Kernel Methods- Support Vector Learning, B. Scholkopf. C.J.C. Burges, A.J. Smola, Eds. MIT Press, Cambridge, pp. 255-268
12 V.N. Vapnik, 1982, Estimation of Dependences Based on Empirical Data, Springer-Verlag
13 Platt. J.C., Cristianini. N. and ShaWe-Talyor, J., 2000, 'Large Margin DAG's for Multiclass Classification.' Advances in Neural Information Processing Systems, Vol. 12, pp. 547-553
14 Schwenker. F., 2000, 'Hierarchical Support Vector Machines for Multi-class Pattern Recognition.' Proceeding of 4th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, pp. 561-565
15 Yang, B. S., Han, T. and An, J. L.. 2004, 'ART-Kohonen Neural Network for Fault Diagnosis of Rotating Machinery.' Mechanical Systems and Signal Processing, Vol. 18. No. 3. pp. 645-657   DOI   ScienceOn
16 Yang, B.S., Kim, K. and Rao, Rai B.K.N., 2002. 'Condition Classification of Reciprocating Compressors Using RBF Neural Network,' International Journal of COMADEM, Vol. 5, No. 4, pp. 12-20
17 이종민, 황요하, 김승종, 송창섭, 2003, 'AR계수를 이용한 Hidden Markov Model의 기계상태진단적용,' 한국소음진동공학회논문집, 제 13권, 제 1호,pp. 48-55
18 Knerr. S.. Personnaz, L. and Dreyfus, G., 1990, Single-layer Learning Revisited: A Stepwise Procedure for Building and Training a Neural Network. in Neuro-computing: Algorithms, Achitectures and Applications, J. Fogelman. Ed.Springer-Verlag, New York
19 Hsu. C.W. and Lin, C.J.. 2002. 'A Comparison of Methods for Multiclass Support Vector Machines' IEEE Transaction on Neural Networks, Vol. 13, No.2, pp. 415-425   DOI   ScienceOn