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.

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Permanently installed ultrasonic sensors have the capability of measuring much smaller changes in the signal than conventional sensors that are used for ultrasonic inspections. This is because uncertainties associated with coupling fluids and positional offsets are eliminated. Therefore it is potentially possible to monitor the onset of material degradation. A particular degradation mechanism that we are keen to monitor is high temperature hydrogen attack; where the amount of damage is linked to a drop in ultrasonic velocity which we hope can be monitored for with an ultrasonic array. The changes introduced in the ultrasonic propagation velocity are expected to be of the order of 1 % and in practice they are observable only from a very limited field of view (i.e. from the outside of a pipe) and therefore the reconstruction is challenging to accomplish. In order to explore the feasibility of this, we are investigating the reconstruction of a non-uniform temperature distribution which allows us to quickly evaluate the sensitivity of our method to small spatial variations in ultrasonic velocity of the material. Two reconstruction algorithms were implemented and their performance compared in simulated and real measurements. The results of the tests were encouraging: local temperature differences as low as 10 • C could be detected, which corresponds to a local propagation velocity change of 5 m/s (0.15 % relative velocity change).