Data visualization, data reduction and classifier fusion for intelligent fault diagnosis in gas turbine engines

Journal of Engineering for Gas Turbines and Power

Volumn 130, Issue 4, 2008, Pages

  Donat, William ^a   Choi, Kihoon ^a   An, Woosun ^a   Singh, Satnam ^a   Pattipati, Krishna ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; CONFORMAL MAPPING; CURVE FITTING; DATA VISUALIZATION; ELECTRIC FAULT CURRENTS; ELECTRIC GENERATORS; ELECTRONICS INDUSTRY; FUSION REACTIONS; GAS ENGINES; GAS TURBINES; KETONES; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; NETWORK PROTOCOLS; NEURAL NETWORKS; POWER ELECTRONICS; PRINCIPAL COMPONENT ANALYSIS; SELF ORGANIZING MAPS; SENSOR NETWORKS; STATISTICS; SUPPORT VECTOR MACHINES; TURBOFAN ENGINES;

A-PRIORI; ADAPTIVE BOOSTING; CLASSIFICATION PERFORMANCE; CLASSIFICATION TECHNIQUES; CLASSIFIER FUSIONS; COMPUTATIONALLY EXPENSIVE; DATA SETS; DATA-DRIVEN; DATA-DRIVEN APPROACHES; DIAGNOSTIC ACCURACY; ELECTRONIC CONTROL UNITS; FAULT CLASSIFICATION; FUSION METHODS; GAS TURBINE ENGINES; GAUSSIAN MIXTURE MODELING; INTELLIGENT FAULT DIAGNOSIS; K-NEAREST NEIGHBOR; MEMORY REQUIREMENTS; MULTIWAY PARTIAL LEAST SQUARES; PRATT AND WHITNEY; PRINCIPAL COMPONENTS; PROBABILISTIC NEURAL NETWORK; REDUCTION TECHNIQUES; S INGLE FAULT; SELF-ORGANIZING; TEST CASES;

DATA REDUCTION;

EID: 53649105730     PISSN: 07424795     EISSN: 15288919     Source Type: Journal    
DOI: 10.1115/1.2838993     Document Type: Article

References (29)

1. Butler, S. W., Pattipati, K. R., Volponi, A., Hull, J., Rajamani, R., and Siegel, J., 2006, "An Assessment Methodology for Data Driven and Model-Based Techniques for Engine Health Monitoring," ASME Paper No. GT 2006-91096.
2. Fodor, I. K., "A Survey of Data Reduction Techniques," available online at: http://www.llnl.gov/casc/sapphire/pubs148494.pdf
3. Basserville, M., and Nikiforov, I., 1993, Detection of Abrupt Changes: Theory and Application, Information and System Science Series, Prentice-Hall, Englewood Cliffs, NJ.
4. Bishop, C. M., 2006, Pattern Recognition and Machine Learning, Springer, New York.
5. Duda, R. O., Hart, P. E., and Stork, D. G. 2001, Pattern Classification, 2nd ed., Wiley, New York.
6. Choi, K., Luo, J., Pattipati, K. R., Qiao, L., and Chigusa, S., 2006, "Data Reduction Techniques for Intelligent Fault Diagnosis," 2006, Proceedings of the IEEE Autotestcon, Anaheim, CA, Sept.
7. Jackson, J. E., 1991, A User's Guide to Principal Components, Wiley, New York.
8. Shah, J., 2004, "Sequential k-Nearest Neighbor Pattern Recognition for Usable Speech Classification - A Revised Report," Speech Processing Lab, Temple University.
9. Nomikos, P., 1996, "Detection and Diagnosis of Abnormal Batch Operations Based on Multi-Way Principal Component Analysis," ISA Trans., 35(3), pp. 259-266.
10. Volponi, A., Depold, H., Ganguli, R., and Daguang, C., 2000, "The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study," ASME Paper No. 2000-GT-547.
11. Haykin, S., 1999, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall, Englewood Cliffs, NJ.
12. Kohonen, T., Erkki, O., Simula, O., Visa, A., and Kangas, J., 1996, "Engineering Applications of the Self-Organizing Map," Proc. IEEE, 84(10), pp. 1358-1384.
13. Hand, D. J., 1981, Discrimination and Classification, Wiley, New York.
14. Mardia, K. V., Kent, J. T., and Bibby, J. M., 1995, Multivariate Analysis, Probability and Mathematical Statistics, Academic, New York.
15. Ripley, B. D., 1996, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge.
16. Friedman, J., Hastie, T., and Tibshirani, R., 2001, Elements of Statistical Learning: Prediction, Inference and Data Mining, Springer, New York.
17. Wold, S., Kettaneh, N., Friden, H., and Holmberg, A., 1998, "Modeling and Diagnostics of Batch Processes and Analogous Kinetic Experiments," Chemom. Intell. Lab. Syst., 44(1-2), pp. 331-340.
18. Wold, S., Geladi, K., Esbensen, K., 1987, "Principle Component Analysis," Chemom. Intell. Lab. Syst., 2(1-3), pp. 37-52.
19. Choi, K., Azam, M., Namburu, M., Luo, J., Pattipati, K. R., and Patterson-Hine, A., 2006, "Fault Diagnostics in HVAC Chillers Using Data-Driven Techniques," IEEE Instrum. Meas. Mag., 8(3), pp. 24-32.
20. Rasmus, R., 1996, "Multiway Calibration. Multilinear PLS," Geol. J., 10(1), pp. 47-61.
21. Eibel, G., and Pfeiffer, K.-P., 2005, "Multi-Class Boosting for Weak Classifiers," J. Mach. Learn. Res., 6, pp. 189-210.
22. Tang, Z. B., Pattipati, K. R., and Kleiman, D. L., 1991, "Optimization of Detection Networks: Part I - Tandem Structures," IEEE Trans. Syst. Man Cybern., 21(5), pp. 1045-1059.
23. Tang, Z. B., Pattipati, K. R., and Kleinman, D. L., 1992, "A Distributed M-ary Hypothesis Testing Problem With Correlated Observations," IEEE Trans. Autom. Control, 37(7), pp. 1042-1046.
24. Pete, A., Pattipati, K. R., and Kleinman, D. L., 1994, "Optimization of Detection Networks With Generalized Event Structures," IEEE Trans. Autom. Control, 39(8), pp. 1702-1707.
25. Kuncheva, L. I., 2004, Combining Pattern Classifiers, Wiley, New York.
26. Chou, C. K., and Liu, C. N., 1968, "Approximating Discrete Probability Distributions With Dependence Trees," IEEE Trans. Inf. Theory, 14(3), pp. 463-467.
27. Berman, K. A., and Paul, J. L., 1997, Fundamentals of Sequential and Parallel Algorithms, PWS, Boston.
28. Aylpaydin, E., 2004, Introduction to Machine Learning, MIT Press, Cambridge.
29. Choi, K., Singh, S., Kodali, A., Pattipati, K., Shepard, J., Namburu, S., Chigusa, S., Prokhorov, D., and Qiao, L., 2007, "Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems," IEEE Autotestcon, Baltimore, MD.