Energy Correlated Damage Indices in Fatigue Crack Extent Quantification

Abstract:

Signals received by piezoelectric transducers (PZT) network can be influenced by many factors. Apart from environmental conditions, whose variability should be compensated, significant difference in a signal can be also caused by relative geometry changes of a designed sensors node, e.g. the damage localization and its orientation with respect to sensors location in the node. In the adopted approach a set of damage indices (DIs), carrying marginal signal information content and correlated with the total energy received by a given sensor are proposed. These are sensitive to the two main modes of guided wave interaction with a fatigue crack, i.e. its transmission and reflection from a damage. Detailed description of DIs detection capabilities are delivered in the paper. Two dimensional reduction techniques: Principal Component Analysis and Fishers Linear Discriminant are compared. The results of the data collected from specimen fatigue test are used to compare several classification models based on the emerged effective damage indices.

• Periodical: Key Engineering Materials (Volumes 569-570)
• Pages: 1186-1193
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.569-570.1186
• Online since: July 2013
• Authors: Krzysztof Dragan, Michal Dziendzikowski, Slawomir Klimaszewski, Sylwester Klysz, Artur Kurnyta
• Keywords: Damage Size Estimation, Fatigue Cracks Detection, Piezosensor Networks, Structural Health Monitoring (SHM)

References

[1] M. Giglio, A. Manes, U. Mariani, R. Molinaro, W. Matta, Helicopter fuselage crack monitoring and prognosis through on-board sensor network, Proc. of CM 2009 and MFPT 2009 (The Sixth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies), 23–25 June 2009, Stillorgan Park Hotel, Dublin, Ireland.

[2] W.J. Staszewski, K. Worden, Signal Processing for Damage Detection, in: W.J. Staszewski, C. Boller, G.R. Tomlinson (eds. ), Health Monitoring of Aerospace Structures, John Wiley & Sons Ltd, England, (2004).

DOI: 10.1002/0470092866.ch5

[3] Z. Su, L. Ye, Identification of Damage Using Lamb Waves, Springer-Verlag, Germany, (2009).

[4] L. E. Mujica, J. Rodellar, A. Fernandez, A. Guemes, Q-statistic and T2-statistic PCA-based measures for damage assessment in structures, Struct. Health Monit. Vol. 10(5) (2010), p.539.

DOI: 10.1177/1475921710388972

[5] K. Dragan, M. Dziendzikowski, T. Uhl, The development of the non-parametric classification models for the damage monitoring on the example of the ORLIK aircraft structure, Key Eng. Mat. Vol. 518 (2012), p.358.

DOI: 10.4028/www.scientific.net/kem.518.358

[6] T. Hastie, R. Tibshirani, J. Friedman: The Elements of Staistical Learning: Data Mining, Inference, and Prediction, second ed., Springer Science+Business Media, New York, (2009).

[7] Information on http: /www. statisticalengineering. com/mh1823/MIL-HDBK- 1823A%28200 9%29. pdf.