Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-25T21:49:17.282Z Has data issue: false hasContentIssue false
  • English
  • Français

Using deformation modes to identify cracks in turbine engine compressor disks

Published online by Cambridge University Press:  03 February 2016

R. A. Brockman ,
R. John  and
M. A. Huelsman
R. A. Brockman
Affiliation:
[email protected], University of Dayton Research Institute Dayton, Ohio, USA
R. John
Affiliation:
[email protected], US Air Force Research Laboratory, Materials and Manufacturing Directorate, AFRL/RXLMN, Wright-Patterson Air Force Base, Ohio, USA
M. A. Huelsman
Affiliation:
University of Dayton Research Institute, Dayton, Ohio, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent studies show that analytical predictions of crack growth in rotating components can be used in conjunction with displacement measurement techniques to identify critical levels of fatigue damage. However, investigations of this type traditionally have focused on the detection of damage at known flaw locations. This paper addresses the related problem of estimating damage associated with flaws at unknown locations, through the combined use of analytical models and measured vibration signatures. Because the measured data are insufficient to identify a unique solution for the location and severity of fatigue cracks, the function of the analytical model is to bound the extent of damage occurring at life-limiting locations. The prediction of remaining life based on estimates of worst-case fatigue damage and crack locations also is discussed.

Type
Research Article
Information
The Aeronautical Journal , Volume 113 , Issue 1150: Flight structures fundamental research in the United States of America: A Special Issue – PART II , December 2009 , pp. 811 - 819
Copyright
Copyright © Royal Aeronautical Society 2009 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Ko, J.M. and Ni, Y.Q., Technology developments in structural health monitoring of large-scale bridges, Engineering Structures, October 2005, 27, (12), pp 1715–1725.Google Scholar
2. Chang, P.C., Flatau, A. and Liu, S.C., Review paper: health monitoring of civil infrastructure, Structural Health Monitoring, September 2003, 2, (3), pp 257–267.Google Scholar
3. Giurgiutiu, V., Zagrai, A.N. and Bao, J., Piezoelectric wafer embedded active sensors for aging aircraft structural health monitoring, Structural Health Monitoring, July 2002, 1, (1), pp 41–61.Google Scholar
4. Takeda, N., Tajima, N., Sakurai, T. and Kishi, T., Recent advances in composite fuselage demonstration program for damage and health monitoring in Japan, Structural Control and Health Monitoring, December 2005, 12, (3-4), pp 245–255.Google Scholar
5. Von Flotow, A., Tappert, P., Mercadal, M. and Hardman, W., Successes and failures for LCF rotor burst prognosis using blade tip sensors, 2004, Paper GT-2004-54131, Proceedings ASME Turbo Expo 2004, Vienna, Austria, June 2004.Google Scholar
6. Saavedra, P.N. and CuitiñO, L.A., Vibration analysis of rotor for crack identification, J Vib and Control, 2002, 8, pp 51–67.Google Scholar
7. Gasch, R., A survey of the dynamic behavior of a simple rotating shaft with a transverse crack, J Sound Vib, November 1993, 160, (2), pp 313–332.Google Scholar
8. He, Y., Guo, D. and Chu, F., Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system, Math Comp Simulation, 2001, 57, pp 95–108.Google Scholar
9. Gounaris, G.D. and Papadopoulos, C., Crack identification in rotating shafts by coupled response measurements, Eng Frac Mech, 2002, 69, pp 339–352.Google Scholar
10. Larsen, J.M. and Christodoulou, L., Integrating damage state awareness and mechanism-based prediction, J Materials, March 2004, 56, (3) p 14.Google Scholar
11. Christodoulou, L. and Larsen, J.M., Using materials prognosis to maximize the utilization potential of complex mechanical systems, J Materials, March 2004, 56, (3), pp 15–19.Google Scholar
12. Kacprzynski, G.J., Sarlashkar, A., Roemer, M.J., Hess, A. and Hardman, W., Predicting remaining life by fusing the physics of failure modeling with diagnostics, J Materials, March 2004, 56, (3), pp 29–35.Google Scholar
13. Russ, S.M., Rosenberger, A.H., Larsen, J.M., Berkeley, R.B., Carroll, D., Cowles, B.A., Holmes, R.A., Littles, J.W., Pettit, R.G. and Schirra, J.J., Demonstration of advanced life-prediction and state-awareness technologies necessary for prognosis of turbine engine disks, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems III, 2004, Proceedings SPIE Conference on Smart Structures and Materials NDE for Health Monitoring and Diagnostics, San Diego, California, UK, March 2004.Google Scholar
14. Seker, S., Ayaz, E. and Türkcan, E., Elman’s recurrent neural network applications to condition monitoring in nuclear power plane and rotating machinery, Eng Appl Art Intelligence, 2003, 16, pp 647–656.Google Scholar
15. Seibold, S. and Weinert, K., A time domain method for the localization of cracks in rotors, J Sound Vib, 1996, 195, (1), pp 57–73.Google Scholar
16. Zencrack User’s Manual, Version 7.1, September 2003, Zentech International, Camberley, Surrey, UK.Google Scholar
17. ABAQUS Analysis User’s Manual, Version 6.7, 2007, Dassault Systèmes, Providence, Rhode Island, USA.Google Scholar
18. Luo, J. and Bowen, P., Small and long fatigue crack growth behavior of a PM Ni-based superalloy, Udimet 720, Int J Fatigue, 2004, 26, (2), pp 113–124.Google Scholar