Using diagnostics and prognostics to minimize the cost of ownership of gas turbines

Proceedings of the ASME Turbo Expo

Volumn 2, Issue , 2006, Pages 845-851

  Depold, Hans R ^a   Siegel, Jason ^a  

^a PRATT AND WHITNEY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COST OF OWNERSHIP; PROGNOSTICS; SAFE OPERATIONS; SOFT REMOVAL TIMES;

ALGORITHMS; CONDITION MONITORING; FAILURE (MECHANICAL); FORECASTING; MAINTENANCE; SERVICE LIFE;

GAS TURBINE LOCOMOTIVES;

EID: 33750817602     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/GT2006-91183     Document Type: Conference Paper

References (8)

1. DePold, H.R., Gass, D.F., 1998, The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics, ASME 98-GT-101.
2. Volponi, et al., 2000, The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: a Comparative Study, ASME 2000-GT-547
3. Bishop, C.M., Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1997.
4. Duda, R.O., Hart, P.E., and Stork, D., Pattern Classification, John Wiley & Sons, New York, 2000.
5. Abernethy, R. B., Thompson, J.W., 1973, Uncertainty in Gas Turbine Measurements, AIAA Paper 73-1230.
6. DePold, H., Volponi, A., Siegel, J., Hull, J., Validation Of Diagnostic Data with Statistical Analysis and Embedded Knowledge, ASME 2003-GT-38764.
7. Robert J. Bronson, et. al, data normalization for engine health monitoring, ASME GT2005-68039, 2005
8. Razak, A, Carlyle, J, Model Based Health Monitoring System to Reduce Gas Turbine Ownership Cost, 2000-GT-0627, 2000