A SVD-Based Signal De-Noising Method With Fitting Threshold for EMAT

The electromagnetic acoustic transducer (EMAT) is a powerful and useful non-destructive testing technology for structural health monitoring. However, EMAT has an issue of low efficiency in conversion and its signal is easily affected by noise, which make it difficult to accurately identify and evaluate structural defects. Thereby, signal de-noising preprocessing is essential for the evaluation of defects. In this paper, we proposed an improved singular value decomposition (SVD) de-noising method based on the fitting threshold for EMAT signal. For SVD de-noising method, the key point is to determine the singular value threshold for reconstructing the signal. We applied a segmented regression model to find the appropriate threshold in this approach. To investigate the efficacy of the proposed method, simulation signals and experimental signals are used for verification respectively. A comparative analysis has been under-taken to confirm that the proposed signal de-noising has advantages over other methods in EMAT signal noise reduction, and it is useful for more accurate evaluation of defects.