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 (DPSM). 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. 0.75−1.1mm/year would be measured for a 1mm/year 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.
展开▼
