Identification of Abnormal UGW Signals Using Multi-Scale Progressive Reconstruction Network

The use of ultrasonic guided waves (UGWs) is an efficient damage monitoring technique. Due to their characteristics of a wide monitoring range and low power consumption, UGWs have been widely applied in various structural health monitoring fields. In practice, the transducers and coupling agents used for UGW excitation and reception are prone to failure due to service environmental factors, resulting in abnormal UGW signals. To ensure reliable damage monitoring, this paper proposed an abnormal UGW signal identification method based on the UGW reconstruction errors. First, a multi-scale progressive reconstruction network (MPRN) is proposed to accurately reconstruct normal UGW signals. Leveraging the inherent differences between normal and anomalous UGW signal characteristics, the reconstruction errors increase significantly when abnormal UGW signals are input into the MPRN, which has been trained exclusively on normal data. This discrepancy in reconstruction errors enables the identification of abnormal signals. The experimental results show that sensor failure causes frequency shifts in the received UGW signals. When reconstructing normal UGW signals, the proposed MPRN achieves high fidelity, with an average NRMSE as low as 0.0036 and an average PSNR as high as 40.04 dB. In contrast, when reconstructing abnormal UGW signals, the average NRMSE is no lower than 0.62, and the average PSNR is no higher than 16.67 dB. The proposed reconstruction-error-based abnormal UGW signal identification method achieves a maximum accuracy of 93.43%.