Damage Detection in Thin Plates and Aerospace Structures with the Electro-Mechanical Impedance Method

Abstract

The use of the electro-mechanical (E/M) impedance method for health monitoring of thin plates and aerospace structures is described. As a nondestructive evaluation technology, the E/M impedance method allows us to identify the local dynamics of the structure directly through the E/M impedance signatures of piezoelectric wafer active sensors (PWAS) permanently mounted to the structure. An analytical model for 2-D thin-wall structures, which predicts the E/M impedance response at PWAS terminals, was developed and validated. The model accounts for axial and flexural vibrations of the structure and considers both the structural dynamics and the sensor dynamics. Calibration experiments performed on circular thin plates with centrally attached PWAS showed that the presence of damage modifies the high-frequency E/M impedance spectrum causing frequency shifts, peak splitting, and appearance of new harmonics. Overall-statistics damage metrics and probabilistic neural network (PNN) are used to classify the spectral data and identify damage severity. On thin-wall aircraft panels, the presence of damage influences the sensors E/M impedance spectrum. When crack damage is in the PWAS medium field, changes in the distribution of harmonics take place and when crack damage is in the PWAS near field, changes in both the harmonics and the dereverberated response are observed. These effects are successfully classified with PNN and overall-statistics metrics, respectively. This proves that permanently attached PWAS in conjunction with the E/M impedance method can be successfully used in structural health monitoring to detect the presence of incipient damage through the examination and classification of the high-frequency E/M impedance spectra.

Keywords

electro-mechanical impedance piezoelectric wafer active sensors vibration of circular plates aging aircraft structural health monitoring overall statistics neural networks E/M impedance PWAS EMI SHM IVHM PNN

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References

Chaudhry, Z. , Joseph, T. , Sun, F. and Rogers, C. (1995). Local-area health monitoring of aircraft via piezoelectric actuator/sensor patches . In: Proceedings, SPIE North American Conference on Smart Structures and Materials, San Diego, CA, 26 Feb–3 March, Vol. 2443, pp. 268–276 .

Giurgiutiu, V. and Zagrai, A.N. (2000). Damage detection in simulated aging-aircraft panels using the electro-mechanical impedance technique . In: Proceedings of Adaptive Structure and Material Systems Symposium, ASME Winter Annual Meeting, Orlando, FL, 5–10 Nov.

Raju, V. , Park, G. and Cudney, H.H. (1998). Impedance based health monitoring of composite reinforced structures . In: Proceedings of the 9th International Conference on Adaptive Structures and Technologies, Cambridge, MA, 14–16 October.

Giurgiutiu, V. and Rogers, C.A. (1999). Recent progress in the application of E/M impedance method to structural health monitoring, damage detection and failure prevention. In: 2nd International Workshop of Structural Health Monitoring, Stanford U., CA , 8–10 Sept pp. 298–307.

Park, G. and Inman, D.J. (2001). Impedance-based structural health monitoring, monograph: In: Woo, S.C. (ed), Nondestructive Testing and Evaluation Methods for Infrastructure Condition Assessment, New York, NY: Kluwer Academic Publishers .

Tseng, K.K.-H. , Soh, C.K. , and Naidu, A.S.K. (June 2001). Non-parametric damage detection and characterization using smart piezoceramic material , Smart Material and Structures 11(3), 317–329 .

Liang, C. , Sun, F.P. and Rogers C.A. (January 1994). Coupled electro-mechanical analysis of adaptive material system-determination of the actuator power consumption and system energy transfer , Journal of Intelligent Material Systems and Structures, 5, 12–20 .

Sun, F.P. , Liang, C. and Rogers, C.A. (1994). Experimental modal testing using piezoceramic patches as collocated sensors-actuators . In: Proceeding of the 1994 SEM Spring Conference & Exhibits, Baltimore, MI, 6–8 June, 1994

Zhou, S.W. , Liang, C. and Rogers, C.A. (1996). An impedance-based system modeling approach for induced strain actuator-driven structures . Journal of Vibration and Acoustics, 118(3), 323–331 .

10.

Esteban, J. (July 1996). Analysis of the sensing region of a PZT actuator-sensor, PhD Dissertation, Virginia Polytechnic Institute and State University.

11.

Park, G. , Cudney, H.H. and Inman, D.J. (2000). An integrated health monitoring technique using structural impedance sensors . Journal of Intelligent Material Systems and Structures, 11(6), 448–455 .

12.

Giurgiutiu, V. and Zagrai, A.N. (January 2002). Embedded self-sensing piezoelectric active sensors for on-line structural identification, transactions of ASME . Journal of Vibration and Acoustics, 124, 116–125

13.

Onoe, Mario and Jumonji, Hiromichi (1967). Useful formulas for piezoelectric ceramic resonators and their application to measurement of parameters , IRE, Number 4, Part 2.

14.

Pugachev, S.I. , Ganopolsky, V.V. , Kasatkin, B.A. , Legysha F.F. and Prydko, N.I. (1984). Piezoceramic transducers, handbook, sudostroenie, St.- Petersburg , (in Russian).

15.

IEEE Std. 176 (1987), IEEE Standard on Piezoelectricity, The Institute of Electrical and Electronics Engineers, Inc.

16.

Liessa, A. (1969). Vibration of plates, Published for the Acoustical Society of America through the American Institute of Physics, Reprinted in 1993.

17.

Soedel, W. (1993). Vibrations of plates and shells, Marcel Dekker, Inc.

18.

Rao, J.S. (1999). Dynamics of plates. Marcel Dekker, Inc., Narosa Publishing House .

19.

Itao, K. and Crandall, S.H. (1979). Natural modes and natural frequencies of uniform, circular, free-edge plates , Journal of Applied Mechanics, 46, 448–453 .

20.

Zagrai, A.N. (May 2002). Piezoelectric-wafer active sensor electro-mechanical impedance structural health monitoring, PhD Dissertation, University of South Carolina.

21.

Specht, D.F. (1990). Probabilistic neural networks, Neural Networks, 3, 109–118 .

22.

Lopes, Jr.V. , Park, G. , Cudney, H. and Inman, D. (March 2000). Impedance based structural health monitoring with artificial neural network . Journal of Intelligent Materials Systems and Structures, 11, 206–214 .

23.

Parzen, E. (1962). On Estimation of a probability density function and mode, Annals of Mathematical Statistics, 33, 1065–1076 .

24.

Rasson, J.P. and Lissoir, S. (1998). Symbolic kernel discriminant analysis, NTTS ’98 , International Seminar on New Techniques and Technologies for Statistics.

25.

Dunlea, S. , Moriarty, P. and Fegan, D.J. (2001). Selection of TeV -rays using the kernel multivariate technique . In: Proceedings of ICRC.

26.

Burrascano, P. , Cardelli, E. , Faba, A. , Fiori, S. and Massinelli, A. (2001). Application of probabilistic neural networks to eddy current non destructive test problems , EANN 2001 Conference, Cagliari, Italy, 16–18 July.

27.

Giurgiutiu, V. , Zagrai, A.N. and Bao, J. (August 2002). Embedded active sensors for in-situ structural health monitoring of thin-wall structures , ASME Journal of Pressure Vessel Technology, 124(3), 293–302 .

28.

Park, G. , Sohn, H. , Farrar, C.R. and Inman, D.J. (November 2003). Overview of piezoelectric impedance-based health monitoring and path forward, Shock and Vibration Digest, 35(6), 451–463 .