Informing design using data mining methods

ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2002

Volumn 2, Issue , 2002, Pages 207-215

  Giess, M D ^a   Culley, S J ^a   Shepherd, A ^b  

^a University of Bath   (United Kingdom)

^b Alstom UK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX ENGINEERING; CRITICAL PARAMETER; DATA MINING METHODS; ENGINEERING FIELDS; FORWARD PREDICTION; GAS TURBINE ROTORS; KNOWLEDGE DISCOVERY TECHNIQUES; MANUFACTURING PROCESS; TEST PERFORMANCE; VIBRATION TEST; DESIGN AND MANUFACTURES;

COMPUTER AIDED DESIGN; ENGINEERING; INDUSTRIAL ENGINEERING; MANUFACTURE; PRODUCTION ENGINEERING; TECHNICAL PRESENTATIONS; ASSEMBLY; GAS TURBINES;

ASSEMBLY; DATA MINING;

EID: 0036973885     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (39)

1. Quinlan, J.R. (1986) Induction of Decision Trees. Machine Learning. 1. pp 81-106.
2. Reich, Y., Barai, S.V. (1999) Evaluating Machine Learning Models for Engineering Problems, Artificial Intelligence in Engineering, 13, 257-272
3. Taguchi, G (1986) Introduction to Quality Engineering: Designing Quality into Products and Processes. Asian Productivity Organisation. Tokyo
4. th ed. Oxford University Press. New York.
5. Zhang, Q (2001). Object-oriented database systems in manufacturing: selection and applications. Industrial Management and Data Systems. 101(3). pp 97-105
6. Piatetsky-Shapiro, G. (1999) The Data-Mining Industry Coming of Age. IEEE Intelligent Systems. 14(6). pp 32-34.
7. Fayyad, U. and Stolorz, P. (1997) Data Mining and KDD: Promise and challenges. Future Generation Computer Systems. 13. pp 99-115
8. Chen, M.S, Han, J., Yu, P.S. (1996) Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering. 8(6). pp 866-883.
9. Smyth, P. (2000) Data Mining: data analysis on a grand scale? Statistic Methods in Medical Research. 9. 309-327
10. Dunkel, B., Soparkar, N., Szaro, J., Uthurusamy, R. (1997) Systems for KDD: From concepts to practise. Future Generation Computer Systems. 13. pp 231-242
11. Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. (1996) The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications of the ACM. 39(11). pp 27-34
12. Forcht, K.A. and Cochran, K. (1999) Using data mining and data warehousing techniques. Industrial Management and Data Systems. 99(5). pp 189-196.
13. Stolorz, P, Cheeseman, P. (1998). Onboard Science Data Analysis: Applying Data Mining to Science-Directed Autonomy. IEEE Intelligent Systems.13(5). pp 62-68.
14. Lee, S.J and Siau, K (2001). A review of data mining techniques. Industrial Management and Data Systems. 101 (1). pp 41-46
15. CRISP-DM Consortium (2001) CRISP-DM 1.0: Step-bystep users guide [WWW] http://www.crisp-dm.org. (Jan 1, 2002)
16. Clifton, C. and Thuraisingham, B. (2001) Emerging standards for Data Mining. Computer Standards and Interfaces. 23. pp 187-193.
17. Witten, F and Frank, E (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Academic Press, USA.
18. Kamrani, A., Rong, W., Gonzalez, R. (2001) A genetic algorithm methodology for data mining and intelligent knowledge acquisition. Computers and Industrial Engineering. 40. pp 361-377.
19. McCulloch, W.S. and Pitts, W. (1943) A logical calculus of ideas imminent in nervous activity. Bulletin of Mathematical Biophysics. 5. pp 115-133.
20. Minsky, M. and Papert, S. (1969) Perceptrons. MIT Press, Cambridge, MA, USA
21. Rumelhart, D.E, McClelland, J.L & The PDP Research Group (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations Cambridge, MA. MIT Press
22. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Internal Representations by Error Propagation In: Rumelhart, D.E and McClelland, J.L eds., (1986) Parallel Distributed Processing Vol. 1, Chapter 8. Cambridge, MA. MIT Press.
23. Bertels, K., Neuberg, L., Vassiliadis, S., Pechanek, D.G. (2001) On Chaos and Neural Networks: The Backpropagation Paradigm. Artificial Intelligence Review. 15. pp 165-187.
24. Hornik, K, Stinchcombe, M. White, H. (1989) Multilayer Feedforward Networks are Universal Approximators. Neural Networks. 2. pp 359-366.
25. Looney, C.G. (1996) Advances in Feedforward Neural Networks: Demystifying Knowledge Acquiring Black Boxes. IEEE Transactions on Knowledge and Data Engineering. 8(2). pp 211-226.
26. Huang, S.C. and Huang, Y.F. (1991) Bounds on the Number of Hidden Neurons in Multilayer Perceptrons. IEEE Transactions on Neural Networks. 2(1). pp 47-55.
27. Bishop, C.M. (1995) Regularisation and Complexity Control in Feed-forward Networks. [WWW] http://www.ncrg.aston.ac.uk/People/bishopc/Welcome.html (2 December 2001)
28. Mehrotra, K.G., Mohan, C.K., Ranka, S. (1991) Bounds on the Number of Samples Needed for Neural Learning. IEEE Transactions on Neural Networks. 2(6). Pp 548-558.
29. Malinov, S., Sha, W., McKeown, J.J. (2001) Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural networks. Computational Materials Science. 21. pp 375-394.
30. Reich, Y., Barai, S.V. (2000) A methodology for building neural networks models from empirical engineering data. Engineering Applications of Artificial Intelligence. 13. pp 685-694.
31. Ray, T, Gokarn, R.P., Sha, O.P. (1996) Neural network applications in naval architecture and marine engineering. Artificial Intelligence in Engineering. 1. pp 213-226.
32. Wang, X.Z. (1999). Data Mining and Knowledge Discovery for Process Monitoring and Control. Springer-Verlag, London.
33. Quinlan, J.R. (1996) Improved Use of Continuous Attributes in C4.5. Journal of Artificial Intelligence Research. 4. pp 77-90.
34. Quinlan, J.R.(1999) Simplifying decision trees. International Journal of Human-Computer Studies. 51. pp 497-510.
35. Fu, L. and Shortliffe, E.H. (2000) The Application of Certainty Factors to Neural Computing for Rule Discovery. IEEE Trans. On Neural Networks 11(3). pp 647-657.
36. Andrews, R., Diederich, J., Tickle, A.B. (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems. 8(6). pp 373-389.
37. Tsujino, K and Nishida, S. (1995) Implementation and refinement of decision trees using neural networks for hybrid knowledge acquisition. Artificial Intelligence in Engineering. 9. pp 265-275.
38. Bishop, C.M. (1993) Curvature-Driven Smoothing: A Learning Algorithm for Feed-forward Networks. IEEE Trans. On Neural Networks 4(5). pp 882-8
39. Alstom Power Ltd. (2000) Promotional Material for Typhoon Gas Turbine. Publication No. 122 03/00/Ruddocks.