Browsing by Subject "Fouling localization"

Ultrasonic guided lamb waves can be used to monitor structural conditions of pipes and other equipment in industry. An example is to detect accumulated precipitation on the surface of pipes in a non-destructive and non-invasive way. The propagation of Lamb waves in a pipe is influenced by the fouling on its surface, which makes the fouling detection possible. In addition, multiple helical propagation paths around pipe structure provides rich information that allows the spatial localization of the fouled area. Gaussian Processes (GP) are widely used tools for estimating unknown functions. In this thesis, we propose machine learning models for fouling detection and spatial localization of potential fouled pipes based on GPs. The research aims to develop a systematic machine learning approach for ultrasonic detection, interpret fouling observations from wave signals, as well as reconstruct fouling distribution maps from the observations. The lamb wave signals are generated in physics experiments. We developed a Gaussian Process Regression model as a detector, to determine whether each propagation path is going across the fouling or not, based on comparison with clean pipe. This binary classification can be regarded as one case of the different fouling observations. Latent variable Gaussian Process models are deployed to model the observations over the unknown fouling map. Then Hamiltonian Monte Carlo sampling is utilized to perform full Bayesian inference for the GP hyper-parameters. Thus, the fouling map can be reconstructed based on the estimated parameters. We investigate different latent variable GP models for different fouling observation cases. In this thesis, we present the first unsupervised machine learning methods for fouling detection and localization on the surface of pipe based on guided lamb waves. In these thesis we evaluate the performance of our methods with a collection of synthetic data. We also study the effect of noise on the localization accuracy.