Independent Component Analysis for Improved Defect Detection in Guided Wave Monitoring

Guided wave sensors are widely used in a number of industries and have found particular application in the oil and gas industry for the inspection of pipework. Traditionally this type of sensor was used for one-off inspections, but in recent years there has been a move towards permanent installation of the sensor. This has enabled highly repeatable readings of the same section of pipe, potentially allowing improvements in defect detection and classification. This paper proposes a novel approach using independent component analysis to decompose repeat guided wave signals into constituent independent components. This separates the defect from coherent noise caused by changing environmental conditions, improving detectability. This paper demonstrates independent component analysis applied to guided wave signals from a range of industrial inspection scenarios. The analysis is performed on test data from pipe loops that have been subject to multiple temperature cycles both in undamaged and damaged states. In addition to processing data from experimental damaged conditions, simulated damage signals have been added to "undamaged" experimental data, so enabling multiple different damage scenarios to be investigated. The algorithm has also been used to process guided wave signals from finite element simulations of a pipe with distributed shallow general corrosion, within which there is a patch of severe corrosion. In all these scenarios, the independent component analysis algorithm was able to extract the defect signal, rejecting coherent noise.