Benchmarking diagnostic algorithms

Proceedings of the ASME Turbo Expo

Volumn 1, Issue , 2007, Pages 857-864

  Sambasivan, Lokesh Kumar ^a   Mukherjee, Joydeb ^a   Mylaraswamy, Dinkar ^b  

^a HONEYWELL TECHNOLOGY SOLUTIONS   (India)

^b HONEYWELL TECHNOLOGY CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COST ELEMENTS; DIAGNOSTIC ALGORITHMS;

AEROSPACE INDUSTRY; BENCHMARKING; COST BENEFIT ANALYSIS; DECISION MAKING; FAILURE ANALYSIS; GAS TURBINES;

ALGORITHMS;

EID: 34548753476     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/GT2007-28194     Document Type: Conference Paper

References (14)

1. Simon, H., 1999. Neural Networks: A comprehensive foundation, 2 ed. Prentice-Hall of India.
2. Poggio, T., Rifkin, R., Mukherjee, S., and Niyogi, P., 2004. "General conditions for predictivity in learning theory". Letters to Nature, 428, March, pp. 419-422.
3. Poggio, T., Rifkin, R., Mukherjee, S., and Niyogi, P., 2004. "Stability results in learning theory". World Scientific, 3(48), pp. 397-417.
4. Alpaydin, E., 2005. Introduction to Machine Learning. Prentice-Hall of India, New Delhi,India.
5. Yan, W., Goebel, K., and Li, J. C., 2002. "Classifier performance measures in multi-fault diagnosis for aircraft engines". In Proceedings of SPIE Component and Systems Diagnostics, Prognostics, and Health Management II, P. K. Willett and T. Kirubarajan, eds., SPIE's 16th Annual International Symposium on AeroSpace/Defense Sensing, Simulation, and Controls, Orlando, Florida, pp. 88-97.
6. Drummond, C., and Holte, R. C., 2000. "Explicitly representing expected cost: an alternative to ROC representation". In Knowledge Discovery and Data Mining, pp. 198-207.
7. Provost, F. J., and Fawcett, T., 1997. "Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions". In Knowledge Discovery and Data Mining, pp. 43-48.
8. Webb, I. G., and Ming, T. K., 2005. "On the application of roc analysis to predict classification performance under varying class distributions". Machine Learning, 58(1), January, pp. 25-32.
9. Salzberg, S. L., 1997. "On comparing classifiers: Pitfalls to avoid and a recommended approach". Data Mining and Knowledge Discovery, 1, pp. 317-327.
10. Pal, S. K., and Majumder, D. D., 1977. "Fuzzy sets and decision making approaches in vowel and speaker recognition". IEEE Transactions on Systems, Man, and Cybernetics, 7, pp. 625-629.
11. Menon, S., Mukherjee, J., Kini, V. B., and Eswara, L., 2005. "Gas turbine fault detection and diagnosis using advanced pattern recognition methods". ASME Turbo Expo 2005.
12. Duda, R. O., Hart, P. E., and Stork, D. G., 2000. Pattern Classification, 2 ed. John Wiley & Sons ASIA Pte Ltd, November.
13. Terano, T., Asai, K., and Sugeno, M., eds., 1994. Applied Fuzzy Systems. Academic Press Professional Inc.
14. Anthony, M., and Bartlett, P. L., 1999. Neural Network Learning: Theoretical Foundations. Cambridge University Press, Cambridge, UK.