Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades

Mechanical Systems and Signal Processing

Volumn 21, Issue 5, 2007, Pages 2212-2236

  Pawar, Prashant M ^a   Ganguli, Ranjan ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

Composite rotor blade; Debonding delamination; Genetic fuzzy system; Matrix cracking; Online damage detection

Indexed keywords

AERODYNAMICS; DAMAGE DETECTION; GENETIC PROGRAMMING; HELICOPTER ROTORS; STRUCTURAL HEALTH MONITORING; TURBOMACHINE BLADES;

AEROELASTIC ANALYSIS; COMPOSITE HELICOPTER ROTOR BLADES; GENETIC FUZZY SYSTEMS (GFS); NOISY DATA;

FUZZY SYSTEMS;

EID: 34047276831     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2006.09.006     Document Type: Article

References (45)

1. J.D. Cronkhite, Practical application of health and usage monitoring (HUMS) to helicopter rotor, engine and drive system, Proceedings of the American Helicopter Society 49th Annual Forum, May 19-21, 1993, St. Louis, MO, USA, pp. 1445-1455.
2. J. Land, C. Weitzman, How HUMS have the potential of significantly reducing the direct operating costs for modern helicopters through monitoring, Proceedings of the American Helicopter Society 51st Annual Forum, May 9-11 1995, Fort Worth, TX, USA, pp. 744-757.
3. Samuel P.D., and Pines D.J. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration 282 1-2 (2005) 475-508
4. Alkahe J., and Rand O. Analytic extraction of the elastic coupling mechanisms in composite blades. Composite Structures 49 4 (2000) 399-413
5. Ghiringhelli G.L., Masarati P., and Mantegazza P. Analysis of an actively twisted rotor by multi-body global modelling. Composite Structures 52 1 (2001) 113-122
6. Morozov E.V., Sylantiev S.A., and Evseev E.G. Impact damage tolerance of laminated composite helicopter blades. Composite Structures 62 3-4 (2003) 367-371
7. Azzam H., and Andrew M.J. The use of math-dynamic model to aid the development of integrated health and usage monitoring. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 206 G1 (1992) 71-76
8. Ganguli R., Chopra I., and Haas D.J. Simulation of helicopter rotor-system structural damage, blade mistracking, friction and freeplay. Journal of Aircraft 35 4 (1998) 591-597
9. Yang M., Chopra I., and Haas D.J. Sensitivity of rotor-fault-induced vibrations to operational and design parameters. Journal of the American Helicopter Society 49 3 (2004) 328-339
10. Ganguli R. Health monitoring of helicopter rotor in forward flight using fuzzy logic. AIAA Journal 40 12 (2002) 2773-2781
11. Pawar P.M., and Ganguli R. Genetic fuzzy system for damage detection in beams and helicopter rotor blades. Computer Methods in Applied Mechanics and Engineering 192 16-18 (2003) 2031-2057
12. Alkahe J., Rand O., and Oshman Y. Helicopter health monitoring using an adaptive estimator. Journal of the American Helicopter Society 48 3 (2003) 199-210
13. Hwang H.Y., and Kim C. Damage detection in structures using a few frequency response measurements. Journal of Sound and Vibration 270 1-2 (2004) 1-14
14. Roy N., Ganguli R., and Helicopter. rotor blade frequency evolution with damage growth and signal processing. Journal of Sound and Vibration 283 3-5 (2005) 821-851
15. Cattarius J., and Inman D.J. Experimental verification of intelligent fault detection in rotor blades. International Journal of Systems Science 31 11 (2003) 1375-1379
16. Ghoshal A., Harrison J., Sundaresan M.J., Hughes D., and Schulz M.J. Damage detection testing on a helicopter flexbeam. Journal of Intelligent Material Systems and Structures 12 5 (2001) 315-330
17. Kiddy J., and Pines D. Experimental validation of a damage detection technique for helicopter main rotor blades. Proceedings of the Institution of Mechanical Engineers, Part I Journal of Systems and Control in Engineering 215 I3 (2001) 209-220
18. Lakshmanan K.A., and Pines D.J. Damage identification of chordwise crack size and location in uncoupled composite rotorcraft flexbeams. Journal of Intelligent Material Systems and Structures 9 2 (1998) 146-155
19. Pawar P.M., and Ganguli R. On the effect of matrix cracks in composite helicopter rotor blade. Composites Science and Technology 65 3-4 (2005) 581-594
20. Pawar P.M., and Ganguli R. Modeling multi-layer matrix cracking in thin-walled composite rotor blade. Journal of the American Helicopter Society 50 3 (2005) 354-366
21. Pawar P.M., and Ganguli R. Modeling progressive damage accumulation in thin walled composite beams for rotor blade applications. Composites Science and Technology 66 13 (2006) 2337-2349
22. P.M. Pawar, R. Ganguli, On the effect of progressive damage on composite helicopter rotor system behaviour, Composite Structures, in press, doi:10.1016/j.compstruct.2005.11.043.
23. Garg A.K., Mahapatra D.R., Suresh S., Gopalakrishnan S., and Omkar S.N. Estimation of composite damage model parameters using spectral finite element and neural network. Composites Science and Technology 64 16 (2004) 2477-2493
24. Chakraborty D. Artificial neural network based delamination prediction in laminated composites. Materials and Design 26 1 (2005) 1-7
25. Yuan S.F., Wang L., and Peng G. Neural network method based on a new damage signature for structural health monitoring. Thin-Walled Structures 43 4 (2005) 553-563
26. Su Z.Q., and Ye L. Quantitative damage prediction for composite laminates based on wave propagation and artificial neural networks. Structural Health Monitoring 4 1 (2005) 57-66
27. Ramanujam N., Nakamura T., and Urago M. Identification of embedded interlaminar flaw using inverse analysis. International Journal of Fracture 132 2 (2005) 153-173
28. Iwasaki A., and Todoroki A. Delamination identification of CFRP structure by discriminant analysis using Mahalanobis Distance. Advances in Nondestructive Evaluation, PT 1-3 Key Engineering Materials 270-273 1-3 (2004) 1859-1865
29. Rao M.A., Srinivas J., and Murthy B.S.N. Damage detection in vibrating bodies using genetic algorithms. Computers and Structures 82 11-12 (2004) 963-968
30. Ramu S.A., and Johnson V.T. Damage assessment of composite structures-a fuzzy-logic integrated neural network approach. Computers and Structures 57 3 (1995) 491-502
31. Ganguli R. A fuzzy logic system for ground based structural health monitoring of a helicopter rotor using modal data. Journal of Intelligent Material Systems and Structures 12 6 (2001) 397-407
32. Cordón O., Gomide F., Herrera F., Hoffmann F., and Magdalena L. Genetic Fuzzy Systems, Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (2001), World Scientific, Singapore
33. Cordón O., Gomide F., Herrera F., Hoffmann F., and Magdalena L. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141 1 (2004) 5-31
34. Pawar P.M., and Ganguli R. Matrix cracking detection in thin-walled composite beam using genetic fuzzy system. Journal of Intelligent Material Systems and Structures 16 5 (2005) 381-468
35. G. Bir, et al., University of Maryland Advanced Rotorcraft Code (UMARC) Theory Manual, UM-AERO Report 92-02 1992.
36. Ganguli R. Optimum design of a helicopter rotor for low vibration using aeroelastic analysis and response surface methods. Journal of Sound and Vibration 258 (2002) 327-344
37. Chandra R., and Chopra I. Structural response of composite beams and blades with elastic couplings. Composite Engineering 2 5-6 (1992) 347-374
38. Ganguli R., and Chopra I. Aeroelastic optimisation of a helicopter rotor with two-cell composite blades. AIAA Journal 34 4 (1996) 835-841
39. Adolfsson E., and Gudmundson P. Thermoelastic properties in combined bending and extension of thin composite laminates with transverse matrix cracks. International Journal of Solids and Structures 34 16 (1997) 2035-2060
40. G.P. Cleveland, C. Trammel, Integrated health and usage monitoring system for the SH-60B helicopter, Proceedings of American Helicopter Society 52nd Annual Forum, American Helicopter Society, Alexandria, VA, USA, 1996, pp. 1767-1787.
41. Miles T.J., Lucas M., Halliwell N.A., and Rothberg S.J. Torsional and bending vibration measurements on rotors using laser technology. Journal of Sound and Vibration 266 3 (1999) 441-467
42. Castellini P., Martarelli M., and Tomasini E.P. Laser Doppler vibrometry: development of advanced solutions answering to technology's needs. Mechanical Systems and Signal Processing 20 6 (2006) 1265-1285
43. Cabell R.H., Fuller C.R., and O'Brien W.F. Neural network modelling of oscillatory loads and fatigue damage estimation of helicopter components. Journal of Sounds and Vibration 209 2 (1998) 329-342
44. Azzam H. A practical approach for the indirect prediction of structural fatigue from measured flight parameters. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 211 G1 (1997) 29-38
45. Kosko B. Fuzzy Engineering (1997), Prentice-Hall, NJ