Bearing condition monitoring using classifier fusion

Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2009

Volumn , Issue , 2009, Pages 58-64

  Cococcioni, Marco ^a   Lazzerini, Beatrice ^a   Volpi, Sara Lioba ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

Automatic fault classification; Automatic fault diagnosis; Classifier fusion; Neural networks; Statistical classifiers

Indexed keywords

AUTOMATIC FAULT DIAGNOSIS; BEARING CONDITION MONITORING; CLASSIFIER FUSION; FAULT CLASSIFICATION; FEATURE SELECTION AND CLASSIFICATION; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; MULTICLASSIFIER SYSTEM; ROLLING ELEMENT BEARING; SOFTCOMPUTING TECHNIQUES; STATISTICAL CLASSIFIER; TIME DOMAIN; VIBRATION SIGNAL;

BEARINGS (STRUCTURAL); CLASSIFIERS; CONDITION MONITORING; DEFECTS; ELECTRIC FAULT CURRENTS; FAILURE ANALYSIS; FAST FOURIER TRANSFORMS; FEATURE EXTRACTION; SOFT COMPUTING; TIME DOMAIN ANALYSIS; WAVELET TRANSFORMS;

NEURAL NETWORKS;

EID: 77954000415     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. A. H. C. Tsang, Condition-based maintenance: tools and decision making, Journal of Quality in Maintenance Engineering, 1(3), 1995, 3-17.
2. J. H. Williams, Condition-based maintenance machine diagnostics (Kluwer Academic Publisher, London UK, 1994).
3. B. Li, M.-Y Chow, Y. Tipsuwan & J. C. Hung, Neural-Network-Based Motor Rolling Bearing Fault Diagnosis, IEEE Transactions on Industrial Electronics, 47(5), 2000, 1060-1069.
4. M. Cococcioni, B. Lazzerini, S. L. Volpi, Rolling element bearing fault classification using soft computing techniques. IEEE International Conference on Systems, Man, and Cybernetics, (SMC 2009), Texas, USA, 2009.
5. B. Samanta, K. R. Al-Balushi & S.A. Al-Araimi, Artificial neural networks and genetic algorithm for bearing fault detection, Soft Computing, 10(3), 2006, 264-271.
6. B. Samanta, K. R. Al-Balushi & S.A. Al-Araimi, Bearing fault detection using artificail neural networks and generic algorithm, Journal on Applied Signal Processing, 3, 2004, 366-377.
7. N. Nguyen & Lee H., Bearing fault diagnosis using adaptive network besed fuzzy inference system. International Symposium on Electrical & Electronics Engineering, HCM City, Vietnam, 2007.
8. N. Tandon & A. Choudhury, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribology International, 32(8), 1999, 469-480.
9. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms (Wiley Interscience, New Jersey, USA, 2004).
10. nd edition (John Wiley & Sons, New York, 2001).
11. A. Webb, Statistical Pattern Recognition, 2nd edition (John Wiley & Sons, New York, 2002).
12. C. Liu & H. Wechsler, Robust Coding Schemes for Indexing and Retrieval from Large Face Databases, IEEE Transactions on Image Processing, 9(1), 2000, 132-136.
13. T. G. Dietterich, Ensemble method in machine learning. Lecture Notes in Computer Science, Workshop on Multiple Classifier Systems, 2000, 1-15.
14. R. P. W. Duin, P. Juszczak & P. Paclik, PRTools4: A Matlab Toolbox for Pattern Recognition, version 4.1, (Delft Pattern Recognition Research, Delft University of Technology, Delft, The Netherlands, 2007).