Principal Component Analysis of a Piezosensor Array for Damage Localization

Abstract

This paper focuses on applying statistical process control techniques, based on principal component analysis, to vibration-based damage diagnosis. It is well known that localized structural damages with relative small amplitude do not affect much the global modal response of the structure, at least at low frequencies. Nevertheless, it can be expected that the local dynamic behavior of a damaged structural subcomponent is significantly affected. Assuming that each structural subcomponent is monitored, local structural damage, with relative small amplitude, will only affect a particular sensor without affecting significantly the response of the others. By applying a principal component analysis on the sensor time responses, it is possible to see that any change of one particular sensor will affect the subspace spanned by the complete sensor response set. The subspace corresponding to the damaged structure can then be compared with the subspace of an initial state in order to diagnose possible damage. The principal component analysis may also be performed for every potential subset of damaged sensors in order to identify the involved sensor, and, therefore, the damaged structural component. The spatial information given by the distributed sensors (e.g. piezoelectric laminates) can be used to forecast structural damages on a critical area but damage localization is also possible with classical sensors (e.g. accelerometers). The damage may be located as the errors attain the maximum at the sensors instrumented in the damaged substructures.

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References

1. Friswell, M.I. and Penny, J.E.T. (1997). Is damage location using vibration measurements practical? Presented at DAMAS 97, Structural Damage Assessment using Advanced Signal Processing Procedures, Sheffield, June/July 1997.

2. Lee, C.-K. (1992). Piezoelectric laminates: theory and experiments for distributed sensors and actuators. In: Tzou and Anderson (eds.), Intelligent structural systems, pp. 75-167, Kluwer Academic Publishers, Boston .

3. Lenaerts, V., Kershen, G. and Golinval, J.-C. (2001). Proper orthogonal decomposition for model updating of non-linear mechanical systems . Mechanical Systems and Signal Processing, 15(1), 31-43 .

4. Friswell, M.I. and Inman, D.J. (2000). Sensor validation for smart structures. Presented at the 18th International Modal Analysis Conference (IMAC-XVIII), San Antonio, February 2000.

5. Saunders, R.S., Cole, D.G. and Robertshaw, H.H. (1994). Experiments in piezostructure modal analysis for MIMO feedback control . Smart Mater. Struct., 3, 210-218 .

6. Géradin, M. and Rixen, D. (1994). Mechanical vibrations: theory and application to structural dynamics. pp. 52-54, John Wiley & Sons, Ltd., Chichester .

7. Golub, G.H. and Van Loan, C.F. (1996). Matrix computations. pp. 603-604, The Johns Hopkins University Press, Baltimore .

8. Sohn, H., Worden, K. and Farrar, C.R. (2001). Novelty detection under changing environmental conditions . Presented at the SPIE's 8th Annual International Symposium on Smart Structures and Materials, Newport Beach, March 2001.

9. Tzou, H.S. and Tseng, C.I. (1991). Distributed vibration control and identification of coupled elastic/piezoelectric systems: finite element formulation and applications . Mechanical Systems and Signal Processing, 5(3), 215-231 .