The effect of corrosion induced surface morphology changes on ultrasonically monitored corrosion rates

Abstract

Corrosion rates obtained by very frequent (daily) measurements with permanently installed ultrasonic sensors have been shown to be highly inaccurate when changes in surface morphology lead to ultrasonic signal distortion. In this paper the accuracy of ultrasonically estimated corrosion rates (mean wall thickness loss) by means of standard signal processing methods (peak to peak-P2P, first arrival-FA, cross correlation-XC) was investigated and a novel thickness extraction algorithm (adaptive cross-correlation-AXC) is presented. All of the algorithms were tested on simulated ultrasonic data that was obtained by modelling the surface geometry evolution coupled with a fast ultrasonic signal simulator based on the distributed point source method. The performance of each algorithm could then be determined by comparing the actual known mean thickness losses of the simulated surfaces to the values that each algorithm returned. The results showed that AXC is the best of the investigated processing algorithms. For spatially random thickness loss 90% of AXC estimated thickness trends were within −10 to +25% of the actual mean loss rate (e.g. 0.75-1.1 mm year −1 would be measured for a 1 mm year −1 actual mean loss rate). The other algorithms (P2P, FA, XC) exhibited error distributions that were 5-10 times larger. All algorithms performed worse in scenarios where wall loss was not distributed randomly in space (spatially correlated thickness loss occured) and where the overall rms of the surface was either growing or declining. However, on these surfaces AXC also outperformed the other algorithms and showed almost an order of magnitude improvement compared to them.

Improving sensitivity and coverage of structural health monitoring using bulk ultrasonic waves

“…Another problem that it will be important to address in some cases is corrosion causing changes in the roughness profile of the back wall and hence in the echo shape as well as its arrival time. 32 It has been shown that it is possible to obtain reliable thickness changes even in the presence of varying roughness profiles, 33 but the changing nature of the back wall echo will make it impossible to detect small defects from changes in the back wall signal or the ratio of successive signals. Therefore, alternative compensation schemes will have to be devised.…”

Fatigue State Characterization of Steel Pipes Using Ultrasonic Shear Waves

“…The peaks of this autocorrelated signal were used to determine the speed by assuming a constant plate thickness of 8 mm, and dividing by the time difference between those peaks. Autocorrelation was used instead of a simple timedomain analysis as it provides better signal-to-noise ratios and is less prone to errors arising from dispersion or signal distortion [25], [26].…”

Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines

“…Finally, Ref. [37] proposes a non-standard approach based on cross-correlation, but, instead of performing the correlation with the transmitted signal, they correlate a back-wall echo with a reference signal that has been modified using an iterative algorithm that also involves cross-correlations. Thus, this approach achieves wall-thickness estimations (below 1 µm) on ultrasonic signals.…”