Adaptive neural network approach to on-line learning for process fault diagnosis

Transactions of the Institute of Measurement & Control

Volumn 20, Issue 3, 1998, Pages 144-152

  Gomm, J B ^a  

^a NONE

Author keywords

fault diagnosis; Neural networks; on line adaptation; self learning systems

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; LEARNING SYSTEMS; ONLINE SYSTEMS; PROCESS CONTROL;

ADAPTIVE NEURAL NETWORKS; PROCESS FAULT DIAGNOSIS;

NEURAL NETWORKS;

EID: 0032276130     PISSN: 01423312     EISSN: None     Source Type: Journal    
DOI: 10.1177/014233129802000305     Document Type: Article

References (17)

1. Foss, B.A., and Johansen, T.A. 1992. ‘An integrated approach to on-line fault detection and diagnosis- including artificial neural networks with local basis functions ‘. Proc IFAC Int Symp on On-line Fault Detection and Supervision in the Chemical Process Industries, Newark DE, USA, 22-24 April, 207-212.
2. Frank, P.M. 1996. ‘Analytical and qualitative model-based fault diagnosis - a survey and some new results’. European Journal of Control, 2(1), 6-28.
3. Gomm, J.B. 1994. ‘Self-learning RBF neural network for on-line process fault diagnosis’. Proc 5th Int Conf on Process Systems Engineering (PSE'94), Kyonju, South Korea, 30 May-3 June, Vol 2, 931-936.
4. Gomm, J.B., Weerasinghe, M., and Williams, D. 1998. Pruning and extraction of qualitative fault diagnosis rules from a trained neural network. Proc IFAC Int Workshop on On-line Fault Detection and Supervision in the Chemical Process Industries, Lyon, France, 4-5 June, Vol 1.
5. Hoskins, J.C., and Himmelblau, D.M. 1988. ‘Artificial neural network models of knowledge representation in chemical engineering’. Computers Chem Engng, 12, 881-890.
6. Himmelblau, D.M., Barker, R.W., and Suewatanakul, W. 1991. ‘Fault classification with the aid of artificial neural networks’, Proc IFAC/IMACS Symp SAFEPROCESS''91, Baden-Baden, Germany, 10-13 Sept, 541-545.
7. Jang, J.S.R., and Sun, C.-T. 1993. ‘Functional equivalence between radial basis function networks and fuzzy inference systems’, IEEE Trans Neural Networks, 4(1), 156-159.
8. Lee, Y., and Lippman, R.P. 1989. ‘Practical characteristics of neural networks and conventional pattern classifiers on artificial and speech problems’, Proc IEEE Conf on Neural Information Processing Systems, 168-177.
9. Leonard, J.A., and Kramer, M.A. 1991. ‘Radial basis function networks for classifying process faults’. IEEE Control Systems Magazine, 11(3), 31-38.
10. Ljung, L., and Soderstrom, T. 1983. Theory and Practice of Recursive Identification, MIT Press.
11. Martin, E.B., Morris, A.J., and Zhang, J. 1996. ‘Process performance monitoring using multivariate statistical process control’, IEE Proc D, 143(2), 132-144.
12. Moody, J., and Darken, C.J. 1989. ‘Fast learning in networks of locally-tuned processing units’. Neural Computation, 1, 281-294.
13. Sorsa, T., Koivisto, H., and Koivo, H.N. 1991. ‘Neural networks in process fault diagnosis’. IEEE Trans Systems, Man and Cybernetics, 21(4), 815-825.
14. Sorsa, T., Suontausta, J., and Koivo, H.N. 1993. ‘Dynamic fault diagnosis using radial basis function networks ‘. Proc TOOLDIAG'93, Toulouse, France, 5-7 April, 160-169.
15. Vaidyanathan, R., and Venkatasubramanian, V. 1992. ‘Representing and diagnosing dynamic process data using neural networks’. Engng Applic Artif Intell, 5(1), 11-21.
16. Weerasinghe, M., Gomm, J.B., and Williams, D. 1998. ‘Neural networks for fault diagnosis of a nuclear fuel processing plant at different operating points’. Control Engineering Practice, 6, 281-289.
17. Zhang, J., and Roberts, P.D. 1992. ‘On-line process fault diagnosis using neural network techniques’. Trans Inst M C, 14(4), 179-188.