Coating degradation monitoring is increasingly important in offshore industries, where protective layers ensure corrosion prevention and structural integrity. In this context, coating thickness estimation provides critical information. The ultrasound pulse-echo technique is widely used for non-destructive testing (NDT), but closely spaced acoustic interfaces often produce overlapping echoes, which complicates detection and accurate isolation of each layer’s thickness. In this study, analysis of the pulse-echo signal from a coated sample has shown that the front-coating reflection affects each main backwall echo differently; by comparing two consecutive backwall echoes, we can cancel the acquisition system’s impulse response and isolate the propagation path-related information between the echoes. This work introduces an ultrasound echo-based methodology for estimating coating thickness by first obtaining the impulse response of the test medium (reflectivity sequence) through a deconvolution model, developed using two consecutive backwall echoes. This is followed by an enhanced detection of coating layer thickness in the reflectivity function using a 1D convolutional neural network (1D-CNN) trained with synthetic signals obtained from finite-difference time-domain (FDTD) simulations with k-Wave MATLAB toolbox (v1.4.0). The proposed approach estimates the front-side coating thickness in steel samples coated on both sides, with coating layers ranging from $60\mathsf{\mu }$m to $740\mathsf{\mu }$m applied over 5 mm substrates and under varying coating and steel properties. The minimum detectable thickness corresponds to approximately $\mathit{\lambda }/5$ for an 8 MHz ultrasonic transducer. On synthetic signals, where the true coating thickness and speed of sound are known, the model achieves an accuracy of approximately $8\mathsf{\mu }$m. These findings highlight the strong potential of the model for reliably monitoring relative thickness changes across a wide range of coatings in real samples.

Pitting corrosion is a localized degradation mechanism that can significantly affect the structural integrity of steel components while remaining difficult to detect and characterize using conventional monitoring approaches. This work investigates the use of pulse-echo ultrasound and data-driven signal analysis for the observability and characterization of localized pitting in low-carbon steel. The ultrasonic response of two 20 MHz single-element contact transducers was investigated through laboratory measurements on artificial pits and physics-based k-Wave simulations spanning a wider range of pit sizes and morphologies. The results show that localized pits modify the received signal through attenuation, scattering, waveform distortion, and the generation of secondary reflections, with the observed ultrasonic response strongly dependent on pit morphology and transducer aperture. A signal-processing and feature-extraction framework was developed to analyze these changes and to evaluate pit observability prior to characterization. Using pit-free measurements, observability limits were quantified with an autoencoder trained on signal descriptors, demonstrating that pit observability is influenced predominantly by pit geometry, normalized pit width, and pit volume rather than by depth alone. For signals exhibiting sufficiently observable and specular pit reflections, time-of-flight measurements enabled pit-depth estimation for defects deeper than 300 µm. To account for propagation-path distortions affecting pit-related echoes, an empirically derived cross-path delay compensation approach was developed using Random Forest models trained on synthetic signals, reducing systematic depth-estimation errors in the synthetic dataset and providing a physics-informed framework for future quantitative characterization studies. Additional Random Forest models employing waveform descriptors and reflection-amplitude indicators were used to assess pit morphology and acoustic surface specularity. The proposed framework was evaluated using laboratory measurements and through preliminary long-term monitoring of two pre-pitted specimens deployed in the HarshLab offshore environment. In these deployments, the methodology enabled simultaneous tracking of localized pit-wall evolution and overall backwall thickness changes from the same pulse-echo measurements, illustrating the potential of the approach for monitoring localized corrosion activity alongside generalized specimen wall loss.