A novel time-of-flight auto-diagnosis model for ultrasonic signal using a sliding window technique based on empirical mode decomposition

Abstract

Ultrasonic signal analysis is an essential technique used in various fields, including non-destructive testing (NDT), structural health monitoring, and medical diagnosis. Accurate auto-diagnosis of Time-of-Flight (ToF) for ultrasonic signal is crucial in defects locating, ultrasound imaging, etc. However, requisite signal extraction for ToF estimation was interfered with noise in time and frequency domain, and there is a tendency to be interrupted by spikes in ultrasound signal envelope for wave crest auto-location. In this study, a novel auto-diagnosis model for ultrasonic signals is proposed based on the empirical mode decomposition (EMD) technique and sliding window approach. The model utilizes EMD method to extract intrinsic mode functions (IMFs) and obtain time-frequency representations of the signal. Then the ultrasound signal envelope is extracted by Hilbert transform. The sliding window technique is employed to monitor the time-varying characteristics of the ultrasonic signal and capture the changes in ToF. Spline interpolation algorithm was embedded in the sliding window technique for reducing ToF error and computer burden. The proposed model can automatically estimate ToF for the ultrasonic signal without professional assistance. The performance of the proposed model is evaluated using steel plates with a thickness of 10mm and pipelines with a wall thickness of 7mm. The results demonstrate high accuracy in detecting signal abnormalities, with maximum errors of 0.25% and 1.25% for steel plates and pipelines under constant pressure, respectively. The proposed approach holds promising potential for fully automated applications in various fields in the future, such as structural health monitoring and medical diagnosis.