In-Flight Vibration Monitoring of Aeronautical Structures

IEEE Control Systems

Volumn 27, Issue 5, 2007, Pages 27-42

  Basseville, Michele ^a   Benveniste, Albert ^a   Goursat, Maurice ^a   Mevel, Laurent ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; FLIGHT DYNAMICS; FLUTTER (AERODYNAMICS); VIBRATIONS (MECHANICAL);

AEROELASTIC STRUCTURES; AERONAUTICAL STRUCTURES; FLUTTER MONITORING; IN-FLIGHT VIBRATION MONITORING;

AEROSPACE ENGINEERING;

EID: 35148853136     PISSN: 1066033X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCS.2007.904652     Document Type: Article

References (57)

1. K.C. Wang and K.W. Iliff, “Retrospective and recent examples of aircraft parameter identification at NASA Dryden Flight Research Center,” AIAA J. Aircraft, vol. 41, no. 4, pp. 752-764,2004.
2. E.A. Morelli and V. Klein, “Application of system identification to aircraft at NASA Langley Research Center,” AIAA J. Aircraft, vol. 42, no. 1, pp. 12–25, 2005.
3. P.G. Hamel and R. Jategaonkar, “Evolution of flight vehicle system identification,” AIAA J. Aircraft, vol. 33, no. 1, pp. 9-28,1996.
4. K.W. Iliff, “Parameter estimation for flight vehicles,” AIAA J. Guid., Contr., Dyna., vol. 12, no. 5, pp. 609-622,1989.
5. NATO Res. Tech. Org., Ed., “System Identification for Integrated Aircraft Development and Flight Testing,” in Proc. RTO/NATO Syst. Concepts and Integration Panel (SCI) Symp., Madrid, Spain, Tech. Rep. RTO-MP-11 AC/323(SCI)TP/7,1999.
6. J.E. Cooper, “Parameter estimation methods for flight flutter testing,” in Proc. AGARD-CP-566, Structures and Materials Panel's Specialists’ Meeting Advanced Aeroservoelastic Testing and Data Anal., Rotterdam, The Netherlands, 1995, pp. 87–99.
7. A. Bucharles and P. Vacher, “Flexible aircraft model identification for control law design,” Aerosp. Sci. Tech., vol. 6, no. 8, pp. 591-598,2002.
8. R. Jategaonkar, D. Fischenberg, and W. von Gruenhagen, “Aerodynamic modeling and system identification from flight data—Recent applications at DLR,” AIAA J. Aircraft, vol. 41, no. 4, pp. 681-691,2004.
9. P. Vacher and A. Bucharles, “A multi-sensor parametric identification procedure in the frequency domain for the real-time surveillance of flutter,” in Proc. 14th IFAC/IFORS Symp. Identification and Syst. Parameter Estimation, Newcastle, Australia, 2006, pp. 636–641.
10. M.B. Tischler and R.K. Remple, Aircraft and Rotorcraft System Identification: Engineering Methods with Flight-test Examples. Reston, VA: AIAA, 2006.
11. C.R. Pickrel and P.J. White. Flight flutter testing of transport aircraft: In-flight modal analysis,” in Proc. 21st Int. Modal Anal. Conf., SEM, Kissimmee, FL, 2003, Paper 200, [CD-Rom].
12. R.K. Mehra and R.K. Prasanth, “Time-domain system identification methods for aeromechanical and aircraft structural modeling,” AIAA J. Air-craft, vol. 41, no. 4, pp. 721–729, 2004.
13. P. Verboven, B. Cauberghe, P. Guillaume, S. Vanlanduit and E. Parloo, “Modal parameter estimation and monitoring for on-line flight flutter analysis,” Mech. Syst. Signal Processing, vol. 18, no. 3, pp. 587-610,2004.
14. B. Peeters and G. De Roeck, “Stochastic system identification for operational modal analysis: A review,” ASME J. Dynamic Syst. Measure. Contr., vol. 123, no. 4, pp. 659–667, 2001.
15. M. Basseville, A. Benveniste, M. Goursat, L. Hermans, L. Mevel, and H. Van der Auweraer, “Output-only subspace-based structural identification: From theory to industrial testing practice,” ASME J. Dynamic Syst. Measure. Contr., vol. 123, no. 4, pp. 668–676, 2001.
16. L. Mevel, A. Benveniste, M. Basseville, and M. Goursat, “Blind sub-space-based eigenstructure identification under nonstationary excitation using moving sensors,” IEEE Trans. Signal Processing, vol. 50, no. 1, pp. 41–48, 2002.
17. A. Benveniste and L. Mevel, “Nonstationary consistency of subspace methods,” IEEE Trans. Automat. Contr., vol. 52, no. 6, pp. 974-984,2007.
18. P. Van Overschee and B. De Moor, Subspace Identification for Linear Systems: Theory-Implementation-Methods. Dordrecht, NL: Kluwer, 1996.
19. P. Van Overschee and B. De Moor, “N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems,” Automatica, vol. 30, no. 1, pp. 75-93,1994.
20. M. Viberg, “Subspace-based methods for the identification of linear time-invariant systems,” Automatica, vol. 31, no. 12, pp. 1835-1853,1995.
21. M. Viberg, B. Wahlberg, and B. Ottersten, “Analysis of state space system identification methods based on instrumental variables and subspace fitting,” Automatica, vol. 33, no. 9, pp. 1603-1616,1997.
22. B. Ottersten M. Viberg and T. Kailath, “Analysis of subspace fitting and ML techniques for parameter estimation,” IEEE Trans. Signal Processing, vol. 40, no. 3, pp. 590-600,1992.
23. R.K. Mehra, “Identification of state space models using a stochastic realization algorithm with application to modal identification of flexible aero-space structures,” in Proc. 9th IFAC/IFORS Symp. Identification and Syst. Parameter Estimation, Budapest, Hungary, 1991, pp. 550–555.
24. E.L. Lehmann, Testing Statistical Hypotheses, 2nd ed. New York: Springer-Verlag, 1986.
25. C.R. Farrar, S.W. Doebling, and D.A. Nix, “Vibration-based structural damage identification,” Philos. Trans. R. Soc. London A, Math. Phys. Sci, vol. 359, no. 1778, pp. 131–150, 2001.
26. M. Basseville, L. Mevel, and M. Goursat, “Statistical model-based damage detection and localization: Subspace-based residuals and damage-to-noise sensitivity ratios,” J. Sound Vib., vol. 275, no. 3–5, pp. 769–794, 2004.
27. Eureka project 3341 FliTE2 description [Online]. Available: http://www.eureka.be/inaction/AcShowProject.do?id=3341
28. M.W. Kehoe, “A historical overview of flight flutter testing,” NASA TM-4720, Oct. 1995. [Online]. Available: http://dtrs.dfrc.nasa.gov/archive/00000231/
29. R. Lind, “Flight test evaluation of flutter prediction methods,” J. Aircraft, vol. 40, no. 5, pp. 964–970, 2003.
30. R. Lind and M. Brenner, “Flutterometer: An on-line tool to predict robust flutter margins,” J. Aircraft, vol. 37, no. 6, pp. 1105-1112,2000.
31. G. Dimitriadis and J.E. Cooper, “Flutter prediction from flight flutter test data,” J. Aircraft, vol. 38, no. 2, pp. 355–367, 2001.
32. M. Prevosto, M. Olagnon, A. Benveniste, M. Basseville, and G. Le Vey, “State space formulation, a solution to modal parameter estimation,” J. Sound Vib., vol. 148, no. 2, pp. 329-342,1991.
33. M. Basseville, M. Abdelghani, and A. Benveniste, “Subspace-based fault detection algorithms for vibration monitoring,” Automatica, vol. 36, no. 1, pp. 101–109, 2000.
34. B.L. Ho and R.E. Kalman, “Effective construction of linear state-variable models from input/output functions,” in Proc. 3rd Allerton Conf Circuit and Syst. Theory, Monticello, IL, 1965, pp. 449–459.
35. H. Akaike, “Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes,” Annals Inst. Statistical Mathematics, vol. 26, no. 1, pp. 363-387,1974.
36. P. Stoϊca and R.L. Moses, Introduction to Spectral Analysis, Englewood Cliffs, NJ: Prentice Hall, 1997.
37. B. Peeters and G. De Roeck, “Reference-based stochastic subspace identification for output-only modal analysis,” Mech. Syst. Signal Processing, vol. 13, no. 6, pp. 855-878,1999.
38. L. Ljung, System Identification - Theory for the User, 2nd ed. Upper Saddle River, NJ: PTR Prentice Hall, 1999.
39. L. Mevel, M. Goursat, M. Basseville, and A. Benveniste, “Subspace-based modal identification and monitoring of large structures, a Scilab tool-box,” in Proc. 13th IFAC/IFORS Symp. Identification and Syst. Parameter Estimation, Rotterdam, NL, 2003, pp. 1405–1410.
40. M. Basseville and I. Nikiforov. (1995). Detection of Abrupt Changes—Theory and Applications. Available: http://www.irisa.fr/sisthem/kniga/
41. L. Le Cam, Asymptotic Methods in Statistical Decision Theory. New York: Springer-Verlag, 1986.
42. M. Basseville, “On-board component fault detection and isolation using the statistical local approach,” Automatica, vol. 34, no. 11, pp. 1391-1416,1998.
43. 2-tests for fault detection and isolation,” in Proc. 5th IFAC/IMACS Symp. Fault Detection, Supervision Safety Tech. Processes (SAFEPROCESS), Washington, 2003, pp. 211–216.
44. L. Mevel, M. Goursat and M. Basseville, “Stochastic subspace-based structural identification and damage detection and localization-Application to the Z24 bridge benchmark,” Mech. Syst. Signal Processing, vol. 17, no. 1, pp. 143–151, 2003.
45. M. Basseville, “Information criteria for residual generation and fault detection and isolation,” Automatica, vol. 33, no. 5, pp. 783-803,1997.
46. A. Benveniste, M. Metivier, and P. Priouret. Adaptive Algorithms and Stochastic Approximations. New York: Springer-Verlag, 1990.
47. L. Mevel, M. Basseville, and A. Benveniste, “Fast in-flight detection of flutter onset: a statistical approach,” AIAA J. Guid. Contr. Dyna., vol. 28, no. 3, pp. 431-438,2005.
48. A. Turevskiy, E. Feron, and J. Paduano, “Flutter boundary prediction using physical models and experimental data,” AIAA J. Guid. Contr. Dyna., vol. 22, no. 1, pp. 168-171,1999.
49. M. Basseville, M. Goursat, and L. Mevel, “Multiple CUSUM tests for flutter monitoring,” in Proc. 14th IFAC/IFORS Symp. Identification and Syst. Parameter Estimation, Newcastle, Australia, 2006, pp. 624–629.
50. L. Mevel, A. Benveniste, M. Basseville, and M. Goursat, “Using simulations to validate a flutter testing method,” in Proc. 23rd Int. Modal Anal. Conf, Orlando, FL, 2005, Paper 119, [CD-Rom].
51. W. Gersch, “On the achievable accuracy of structural parameter estimates,” J. Sound Vib., vol. 34, no. 1, pp. 63-79,1974.
52. D. Bauer, “Asymptotic properties of subspace estimators,” Automatica, vol. 41, no. 3, pp. 359–376, 2005.
53. E. Reynders, R. Pintelon, G. De Roeck, “Variance calculation of covariance-driven stochastic subspace identification estimates,” in Proc. 25th Int. Modal Anal Conf, Orlando, FL, 2007, Paper 169, [CD-Rom].
54. H. Torii and Y. Matsuzaki, “Flutter margin evaluation for discrete-time systems,” AIAA J. Aircraft, vol. 38, no. 1, pp. 42–47, 2001.
55. R.M. Pidaparti and D. Afolabi, “The role of eigenvectors in aeroelastic analysis—Editorial,” J. Sound Vib., vol. 193, no. 4, pp. 934-940,1996.
56. Cosmad: A Free Modal Analysis and Diagnosis Toolbox. [Online]. Available: http://www.irisa.fr/sisthem/cosmad/
57. Scilab: A Free Scientific Software Package [Online]. Available: http://www.scilab.org/