A novel AE source localization method using clustering detection to eliminate abnormal arrivals
Yichao Rui, Zilong Zhou, Jianyou Lu, Barkat Ullah, Xin Cai
Abstract
Due to the significant effect of abnormal arrivals on localization accuracy, a novel acoustic emission (AE) source localization method using clustering detection to eliminate abnormal arrivals is proposed in the paper. Firstly, iterative weight estimation is utilized to obtain accurate equation residuals. Secondly, according to the distribution of equation residuals, clustering detection is used to identify and exclude abnormal arrivals. Thirdly, the AE source coordinate is recalculated with remaining normal arrivals. Experimental results of pencil-lead breaks indicate that the proposed method can achieve a better localization result with and without abnormal arrivals. The results of simulation tests further demonstrate that the proposed method possesses higher localization accuracy and robustness under different anomaly ratios and scales; even with abnormal arrivals as high as 30%, the proposed localization method still holds a correct detection rate of 91.85%.