**Author details**

*Advances in Structural Health Monitoring*

*focusing technique and (c) total focusing method [72].*

techniques to improve SNR.

*Transmit-receive matrices for the imaging algorithms; (a) common source method (b) synthetic aperture* 

records all possible transmit-receive combinations of UGW data. This data collection matrix is symmetric due to reciprocity (**Figure 12**) and only the lower and upper triangular parts of the matrix need be recorded. This data can then be used to obtain tomography images of the structure or perform sound energy focusing

For sensor arrays Full-Matrix Capture (FMC) is a data acquisition process which

For complex structures and if the data corresponding to the damage state is not known *a priori*, damage detection strategies based on unsupervised algorithms are used. One such strategy is based on the Outlier Analysis (OA) algorithm which extracts damage sensitive features from the UGW signals and aims to identify if they have deviated from their baseline distribution using Mahalanobis squared distance [73]. OA can be applied as univariate and multivariate depending on a number of features. For univariate implementation, root mean square (RMS) of the signal has been successfully used as a damage sensitive feature for detection of corrosion type defects in plates [56] and pipes [74]. To increase the damage sensitivity, multivariate OA is recommended, where a number of features are extracted from the UGW signals and classical methods of multivariate statistics such as principal component analysis (PCA) are applied. For UGW, the features of interest include time-of-flight, frequency centres, energies, modes of scattered waves, and time-frequency spread. A review of the feature extraction approaches based on time-frequency representations such as short-time Fourier transform, Wigner-Ville distribution, Hilbert-Huang transform, and wavelet transform can be found in [75]. Recent advances in the field of artificial intelligence led to researchers formulating defect detection as a machine learning problem. A study using an Artificial Neural Network (ANN) based strategy was applied for damage classification [73] and was reported to outperform OA for damage detection using just one feature. Such supervised machine learning strategies will however require data from the structure with known types and levels of damage, which may not always

This chapter presents the advances in guided wave technology for structural health monitoring of two of the most critical metallic assets, pipelines and storage tanks, in the Oil and Gas industry. These SHM technologies support cost-effective asset integrity management by enabling a condition based maintenance model, moving away from conventional routine inspection. The advances in SHM technologies of pipes and tanks are presented. Operational requirements of these SHM systems

**Figure 12.**

**68**

be present.

**5. Conclusions**

Anurag Dhutti1 , Shehan Lowe1 and Tat-Hean Gan1,2\*

1 Brunel University London, Uxbridge, Middlesex, UK

2 TWI Ltd, Cambridge, UK

\*Address all correspondence to: tat-hean.gan@brunel.ac.uk

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
