Unsupervised and auto-adaptive neural architecture for on-line monitoring. Application to a hydraulic process

Engineering Applications of Artificial Intelligence

Volumn 16, Issue 5-6, 2003, Pages 441-451

  Lurette, Christophe ^a   Lecoeuche, Stéphane ^a,b  

^a UNIV LILLE   (France)

^b ECOLE DES MINES DE DOUAI   (France)

Author keywords

Auto adaptation; Classification; Drift; Fault detection; Hydraulic process; On line monitoring; Unsupervised learning

Indexed keywords

HYDRAULICS; LEARNING SYSTEMS; NEURAL NETWORKS;

ONLINE MONITORING;

ARTIFICIAL INTELLIGENCE;

EID: 0142154964     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0952-1976(03)00064-2     Document Type: Article

References (29)

1. Bailey, S., Watton, J., 2000. Progress of the on-line monitoring of the hydraulic systems of a hot steel strip rolling mill. Proceedings of the FPNI-PhD Symposium, Hamburg, pp. 203-213.
2. Bishop C. Neural Networks for Pattern Recognition. 1995;Oxford University Press, Oxford.
3. Crowther W.J., Edge K.A., Burrows C.R., Atkinson R.M., Woollons D.J. Fault diagnosis of a hydraulic actuator circuit using neural networks an output vector space classification approach. Journal of Systems & Control Engineering. 212(1):1998;57-68.
4. Dobchuck J.W., Nikiforuk P.N., Ukrainetz P.R., Burton R.T. Effect of internal pump dynamics on control piston pressure ripple. 2000;Department of Mechanical Engineering, University of Saskatchewan, Canada.
5. Eltoft T., Rui Defigueiredo J.P. A new neural network for cluster-detection-and-labeling. IEEE Transactions on Neural Networks. 9(5):1998;1021-1035.
6. Frazier, W.G., Medina, E.A., Mullins, W.M., Irwin, R.D., 1997. Modeling and simulation metal-forming equipment. Journal of Materials Engineering and Performance 49 (4).
7. Gomm J.B., Yu D.L. Selecting radial basis function network centers with recursive orthogonal least squares training. IEEE Transactions on Neural Networks. 11(2):2000;306-314.
8. Gupta L., McAvoy M., Phegley J. Classification of temporal sequences via prediction using the simple recurrent neural network. Pattern Recognition. 33(10):2000;1759-1770.
9. Gupta M.M., Homma N., Jin L. Static and Dynamic Neural Networks. From Fundamentals to Advanced Theory:2002;Wiley, New York.
10. He J.Z., Zhou Z.H., Yin X.R., Chen S.F. Neural fault diagnosis. 2000;Conference EANN2000, Kingston, England.
11. Hydro Renée Leduc, 2000. Accumulateurs hydropneumatiques. Précautions maintenance. Ref AQ55 G.
12. Ijas M., Virvalo T. Experimental study of hydraulic pulsation dampers for low frequencies. Proceedings of Sixth Triennal International Symposium on Fluid Control Measurement and Visualization. 1999;Sherbrooke, Canada.
13. Imagine S.A. Analysis and design of fluid power mechatronic systems. AMESIM®, Fluid power simulation software, Training course, Roanne, France.
14. Kaye, M., 1999. Cavitation monitoring in hydraulic machines by vibration analysis. Ph.D Thesis, no. 2112, Lausanne University, Switzerland.
15. Lecoeuche S. Unsupervised classifier for monitoring and diagnostic of time series. 2002;Conference ESANN 2002, Bruges, Belgium.
16. Ljung L. System Identification. Theory for the User. 2nd Edition:1999;Prentice-Hall, Englewood Cliffs, NJ.
17. Looney C.G. Pattern Recognition using Neural Networks. Theory and Algorithms for Engineers and Scientists:1997;Oxford University Press, New York.
18. Lurette C., Lecoeuche S. Improvement of cluster detection and labeling neural network by introducing elliptical basis functions. 2001;Conference ICANN 2001, Vienna, Austria.
19. Mak M.W., Kung S.Y. Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification. IEEE Transactions on Neural Networks. 11(4):2000;961-969.
20. Mao J., Jain A.K. A self-organizing network for hyper-ellipsoïdal clustering (HEC). IEEE Transactions on Neural Networks. 11(4):1996;16-29.
21. Mourre D., Burton R. Investigation of a neural network/statistical condition monitoring technique for a proportional solenoid valve. 2001;Department of Mechanical Engineering, University of Saskatchewan, Canada.
22. Münzer, M.E., 2000. Control of mobile hydraulic cranes. Proceedings of the FPNI-PhD Symposium, Hamburg, pp. 475-483.
23. Patton, R.J., Uppal, F.J., Lopez-Toribio, C.J., 2000. Soft computing approaches to fault diagnosis for dynamic systems: a survey. Fourth IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, Budapest, Hungary.
24. Pham D.T., Chan A.B. Control chart pattern recognition using a new type of self organizing neural networks. Proceedings of the Institution of Mechanical Engineers. 212(1):1998;115-127.
25. Sandt, J., Rinkinen, J., Laukka, J., 1997. Particle and water on-line monitoring for hydraulic system diagnosis. The Fifth Scandinavian International Conference on Fluid Power 1997, Linköping, Sweden, Vol. 3. pp. 257-268.
26. Sjöberg, J., 1996. Non-linear system identification with neural networks. Ph.D. Thesis, Department of Electrical Engineering, Linköping University, Sweden.
27. Stecki, J.S., 1998. Total Contamination Control. Fluid Power Net publication Ltd., Australia, ISBN 0-646-35306-3.
28. Zavarehi, M.K., 1997. On-line condition monitoring and fault diagnosis in hydraulic system components using parameter estimation and pattern classification. Ph.D. Thesis, Department of Mechanical Engineering, University of British Columbia.
29. Zheng N., Zhang Z., Shi G., Qiao Y. Self-creating and adaptive learning of RBF networks. merging soft-competition clustering algorithm with network growth technique. International Joint Conference on Neural Networks:1999;Washington, DC, USA.