Ultrasonic Pipeline Inspection Method Based on Welch Power Spectral Density Estimation for Air-Coupled Systems

Abstract The thicknesses of pipe walls and anti-corrosion coatings, together with local void defects, are important parameters affecting the safe operation of long-distance oil and gas pipelines. To enable non-contact quantitative inspection, this study proposes an air-coupled ultrasonic thickness-resonance method. COMSOL finite-element models were established for single-layer steel, single-layer Plexiglass, Plexiglass–steel bilayer structures, and structures containing void defects with different thicknesses. Fast Fourier transform, combined moving-average–Savitzky–Golay preprocessing, and Welch power spectral density estimation were employed to extract resonance frequencies for thickness and defect-depth inversion. The numerical results show that Welch PSD estimation reduces spectral-estimation fluctuations through segment averaging and improves the stability of resonance-frequency extraction and thickness inversion. The relative thickness-inversion errors for single-layer steel and Plexiglass were within 2% and 2%, respectively, while those for the individual layers of the bilayer structures were within 1%. For void-defect models, resonance components associated with the intact thickness and the residual thickness of defective regions could be distinguished. The defect-depth errors for single-layer steel and Plexiglass were within 2% and 1%, respectively, while those for the bilayer structures were within 2%. Air-coupled ultrasonic through-transmission experiments were further conducted on 6.5 and 8.2 mm single-layer steel plates. The inverted thicknesses were 6.358 and 8.33 mm, corresponding to relative errors of 2.1% and 1.6%, respectively. The experimental results provide preliminary support for the thickness-resonance relationship and frequency-extraction method for single-layer steel plates, whereas the performance of Plexiglass–steel bilayer structures and defect-depth inversion requires further experimental validation.