41. Fatigue crack detection for a structural hotspot
Jinniu Tao1, Yongming Feng2, Kezhong Tang3
1The First Aeronautical Institute of Air Force, Xinyang, Henan, 46400, China
2Beijing Aeronautical Technology Research Center, Beijing, 100076, China
395841 troops, People’s Liberation Army, Jiuquan, Gansu, 435018, China
E-mail: email@example.com, firstname.lastname@example.org, email@example.com
(Received 18 December 2013; received in revised form 9 March 2014; accepted 14 March 2014)
Abstract. This work focus on an unsupervised, data driven statistical approach to detect and monitor fatigue crack growth in lug joint samples using surface mounted piezoelectric sensors. Early and faithful detection of fatigue cracks in a lug joint can guide in taking preventive measures, thus avoiding any possible fatal structural failure. The lug joint samples used in this paper are prepared from an aluminum alloy plate with 6 mm thickness and are instrumented with a surface mounted piezoelectric sensor network. Experiments are conducted on three lug joints under constant fatigue loading. The fatigue loading was stopped every 1000 cycles after any small crack was spotted, and the piezoelectric signals corresponding to narrow-band actuations were acquired at every fatigue-loading-stopped cycle. The on-line damage state at any given fatigue cycle is estimated using a damage index approach as the dynamical properties of a structure change with the initiation of a new crack or the growth of an existing crack. Using the measurements performed on an intact lug joint as baseline, damage indices are evaluated from the frequency response of the lug joint with an unknown damage state. As the damage indices are evaluated, a Bayesian analysis is committed and a statistical metric is evaluated to identify damage state (say crack length).
Keywords: structure health monitoring, fatigue crack detection, structural hotspot, principle component analysis, naive Bayesian classification, damage index.
 National Materials Advisory Board. Aging of U.S. Air Force Aircraft, Final Report, 1997.
 Hess A., Calvello G., Frith P., Engel S., Hoitsma D. Challenges, issues, and lessons learned chasing the “Big P”. Real Predictive Prognostics Part 2, 2006, p. 1‑19.
 Chang P. C., Flatau A., Liu S. C. Review paper: health monitoring of civil infrastructure. Structural Health Monitoring, Vol. 2, 2003, p. 257‑267.
 Su Z., Ye L., Lu Y. Guided Lamb waves for identification of damage in composite structures: a review. Journal of Sound and Vibration Digest, Vol. 295, 2006, p. 753‑780.
 Giurgiutiu V., Zagrai A. N. Characterization of piezoelectric wafer active sensors. J. Intell. Mater. Syst. Struct., Vol. 11, Issue 9, 2000, p. 59‑76.
 Beard S. J., Qing P. X., Chang F. K. Hot spot monitoring for aircraft structures. Proceeding of International Workshop on Smart Materials and Structures Technology, 2004.
 Przemyslaw Kolakowski Two approaches to structural damage identification: model updating versus soft computing. Journal of Intelligent Material Systems and Structures, Vol. 17, Issue 1, 2006, p. 63‑79.
 Smith L. A tutorial on principal component analysis. http://www.itu.dk/courses/SIGB/F2011/ untitled%20folder/Reading/pca-smithTutorial.pdf.
 Banerjee S., Ricci F., et al. A wave propagation and vibration-based approach for damage identification in structural components. Journal of Sound and Vibration, Vol. 322, Issue 1‑2, 2009, p. 167‑183.
 Jayaata K. G., Mohan D., Tapas S. An introduction to bayesian analysis. Springer Science, Business Media, New York, 2006, p. 29‑63.
 Peter C. Bayesian models for categorical data. Wiley Series in Probility and Statistics, John Wiley & Sons, 2005, p. 1‑23.
Cite this article
Tao Jinniu, Feng Yongming, Tang Kezhong Fatigue crack detection for a structural hotspot. Journal of Measurements in Engineering, Vol. 2, Issue 1, 2014, p. 49‑56.
Journal of Measurements in
Engineering. March 2014, Volume 2, Issue 1