An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Abstract

Imaging algorithms for visualization of defects play a significant role in Lamb wave–based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the diversity, statistical, and fuzzy characteristics of the elliptic imaging algorithm are analyzed first; then, an intelligent defect location algorithm is proposed based on the evolutionary strategy and the K-means algorithm. The position of defects can be identified by observing the distribution of individuals. There are six parts in the proposed algorithm, including the data structure design, adaptive population screening, adaptive population reproduction, diversity maintenance mechanism, and cutoff criterion. Considering the statistical and fuzzy characteristics in the detection, several specific input parameters are defined in our algorithm, such as the distance-dependent screening threshold, path-dependent residual vector, and path-independent residual. To maintain the diversity of individuals in the analysis, we have made two adjustments to the evolutionary strategy: one is to optimize the population screening and reproduction steps with the K-means algorithm, and the other is to add a diversity maintenance method into the evolutionary strategy. The effectiveness of the proposed intelligent defect location algorithm is verified by numerical simulations and experiments. Numerical studies indicate that the proposed algorithm has a reliable performance in the detection of defects with different shapes and sizes. In the experimental research, we demonstrate that the efficiency of the proposed algorithm is about 200 times faster than the elliptic imaging algorithm. And the optimum parameter setting of the algorithm is investigated by analyzing the influence of parameter setting on the detection.