This research established a physic-based/data-driven anomaly detection for the purpose of damage detection in Lamb waves. These waves have a complicated, multimodal, and frequency dispersive wave propagation that distorts data and makes analysis challenging. Lamb waves are considered high-dimensional data, taking advantage of pre-knowledge of existing and sparse presentation of Lamb waves in frequency-wave number space; the obtained Lamb wave data are converted to this space using sparse wavenumber analysis. Then, taking advantage of high-dimensional methods, converted data are differentiated from pristine and damaged scenarios. The proposed method is applied to an experimental test result on an aluminum plate obtained from pristine conditions and four damage scenarios. The results show that the signal-to-noise ratio for pristine and damaged Lamp waves shows a significant difference which can be used as an indicator of damage.
1. Rigollet, P.; Giraud, C.; Raton, B.; Chapman, F.L.; Crc, H. Introduction to High-Dimensional Statistics. 2015.
2. Tokognon, A.C.; Gao, B.; Tian, G.Y.; Yan, Y. Structural Health Monitoring Framework Based on Internet of Things: A Survey. IEEE Internet Things J. 2017, 4, 619–635, doi:10.1109/JIOT.2017.2664072.
3. Xia, Y.; Chen, J.; Lu, X.; Wang, C.; Xu, C. Big traffic data processing framework for intelligent monitoring and recording systems. Neurocomputing 2016, 181, 139–146, doi:10.1016/j.neucom.2015.07.140.
4. Harley, J.B.; Moura, J.M.F. Data-driven matched field processing for Lamb wave structural health monitoring. J. Acoust. Soc. Am. 2014, 135, 1231–1244, doi:10.1121/1.4863651.
5. Harley, J.B.; Moura, J.M.F. Sparse recovery of the multimodal and dispersive characteristics of Lamb waves. J. Acoust. Soc. Am. 2013, 133, 2732–2745, doi:10.1121/1.4799805.
33 Page Views
0 PDF Downloads
0 Facebook Shares