Automated novelty detection using a modified Kohonen self organizing map

IMechE Event Publications

Volumn 2004 2, Issue , 2004, Pages 313-322

  Jack, L B ^a   Nandi, A K ^a   Wong, M L D ^b  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

^b Swinburne Sarawak Institute of Engineering   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; CONDITION MONITORING; COSTS; DATABASE SYSTEMS; FEATURE EXTRACTION; NEURAL NETWORKS; ROBUSTNESS (CONTROL SYSTEMS); SENSORS; VIBRATIONS (MECHANICAL);

DUAL-CLASS CLASSIFICATION; MACHINE CONDITION MONITORING (MCM); NOVELTY DETECTION; POWER SPECTRAL DENSITY;

SELF ORGANIZING MAPS;

EID: 20844444654     PISSN: 13561448     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. X. Xu, H. Vanderveldt, R. Allen, An ANS based helicopter transmission diagnostic system, in: Proceedings of the 1997 International Conference on Neural Networks, Houston, Texas, 1997, pp. 2214 2218.
2. T. Kim, C. J. Li, Feed-forward neural networks for fault diagnosis and severity assessment of a screw compressor, Mechanical Systems and Signal Processing 9 (5) (1995) pp. 485 496.
3. A. C. McCormick, A. K. Nandi, Neural network autoregressive modelling of vibrations for condition monitoring of rotating shafts, in: Proceedings of the 1997 International Conference on Neural Networks, Houston, Texas, 1997, pp. 2214 2218.
4. A. Murray, J. Penman, Extracting useful higher order features for condition monitoring using artificial neural networks, IEEE Transactions on Signal Processing 45 (1997) pp. 2821 2828.
5. L. B. Jack, A. K. Nandi, Comparison of neural networks and support vector machines in condition monitoring applications, in: H. C. Pusey, R. B. K. N. Rao (Eds.), Proceedings of COMADEM 2000, MFPT Society, Willowbrook, Illinois, USA, 2000, pp. 721 730.
6. A. C. McCormick, A. K. Nandi, Real time classification of rotating shaft loading conditions using artificial neural networks, IEEE Transactions on Neural Networks 8 (3) (1997) pp. 748 757.
7. I. W. Mayes, Use of neural networks for on-line vibration monitoring. Proceedings of the IMechE: Part A 208 (1994) pp. 267 274.
8. T. Kohonen, Self-Organizing Maps, Springer-Verlag, 1997.
9. J. Nikkila, P. Toronen, S. Kaski, J. Venna, E. Castren, G. Wong, Analysis and visualisation of gene expression data using self-organizing maps, Neural Networks 15 (2002) pp. 953 966.
10. A. Flexer, On the use of self-organizing maps for clustering and visualization, in: Principles of Data Mining and Knowledge Discovery, 1999, pp. 80 88.
11. J. Vesanto, SOM-based data visualization methods, Intelligent-Data- Analysis 3 (1999) pp. 111 26. URL citeseer.nj.nec.com/vesanto99sombased.html
12. S. Zhang, R. Ganesan, G. D. Xistris, Self-organising neural networks for automated machinery monitoring systems, Mechanical Systems and Signal Processing 10 (5) (1996) pp. 517 532.
13. L. B. Jack, A. K. Nandi, Genetic algorithms for feature selection in machine condition monitoring with vibration signals, IEE Proceedings - Vision, Image and Signal Processing 147 (3) (2000) pp. 205 212.
14. L. B. Jack, A. K. Nandi, Support vector machines for detection and characterisation of rolling element bearing faults, Proceedings of the Institution of Mechanical Engineers, Part C 215 (9) (2001) pp. 1065 1074.
15. L. B. Jack, A. K. Nandi, Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechanical Systems and Signal Processing 16 (2-3) (2002) pp. 373 390.