Lamb mode and damage identification using small-sample dictionary algorithm

ABSTRACT

In this paper, Lamb mode identification method based on small-sample dictionary algorithm is proposed and applied for the separation of specific Lamb modes, the reconstruction of Lamb waves upon propagating a certain distance and damage identification. This approach includes the creation of small-sample dictionary and querying procession in a dictionary. Firstly, Lamb wave signals upon propagating at a series of distances are simulated, and signal features, {mode, distance, time of flight (Tof), wavelet energy}, are extracted to create a dictionary; secondly, Tof of the received signal is extracted, and then Lamb modes are identified by searching the dictionary; finally, energy parameters are estimated to reconstruct wavepackets. The feasibility of this algorithm is validated in AAA laminate, and the results are presented. In a 2D-simulation model of a pitch-catch configuration, A₀ and S₀ modes can be identified and reconstructed effectively when the direct waves and the reflected waves are synchronously received, with the propagation distance of 0.3 m and 0.5 m, respectively. In addition, a Lamb-wave-based delamination location is conducted in three-dimensional AAA laminate. The experimental results show that the delamination can be located relatively by combining the identified damage-scattered S₀ waves and the probability-based diagnostic imaging.

KEYWORDS:

• Lamb mode identification
• dictionary learning
• wavelet transform

Acknowledgments

My heartfelt thanks are due to Prof. Han for his academic supervision and personal support. This work was supported by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (Grant No. STIP2020L0699), Fund program of Key Laboratory of Signal Capturing & Processing, North University in Shanxi (Grant No. ISPT2020-8).

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

The work was supported by the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [Grant No. STIP2020L0699]; Fund program of Key Laboratory of Signal Capturing & Processing, North University in Shanxi [Grant No. ISPT2020-8].