Application of support vector machines in reciprocating compressor valve fault diagnosis

Lecture Notes in Computer Science

Volumn 3611, Issue PART II, 2005, Pages 81-84

  Ren, Quanmin ^a   Ma, Xiaojiang ^a   Miao, Gang ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSORS; ELECTRIC FAULT CURRENTS; PROBLEM SOLVING; RELIABILITY; STATISTICAL METHODS;

INTELLIGENT DIAGNOSIS SYSTEM; SUPPORT VECTOR MACHINE (SVM); THE LOCAL WAVE METHOD; VIBRATION SIGNALS;

PATTERN RECOGNITION;

EID: 26844516122     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11539117_13     Document Type: Conference Paper

References (6)

1. Paul, C., Hanlon: Compressors Handbook. China Petrochemical Press (2003)
2. Vapnik, V.N.: The Nature of Statistical Learning Theory. Tsinghua University Press, Beijing (2000)
3. Huang, N.B., Shen, Z., Long, S.R.: The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis. Proc. Royal Soc. London 454 (1998) 903-985
4. Ma, X.J., Yu, B.: A New method for Time-Frequency - Local Wave Method. J. of Vibration Eng. 13 (2000) 219-224
5. Yang, B.S., Han, T., An, J.L., Kim, H. C.: A Condition Classification System for Reciprocating Compressor. Structural Health Monitoring. 3 (3) (2004) 227-284
6. Osuna, E.,Freund, R.,Girosi, F.: An Improved Training Algorithm for Support Vector Machines. Neural Networks for Signal Processing VII. (1997) 276-285