Exploring the application of ultrasonic phased arrays for industrial process analysis

This thesis was previously held under moratorium from 25/11/19 to 25/11/21Typical industrial process analysis techniques require an optical path to exist between the measurement sensor and the process to acquire data used to optimise and control an industrial process. Ultrasonic sensing is a well-established method to measure into optically opaque structures and highly focussed images can be generated using multiple element transducer arrays. In this Thesis, such arrays are explored as a real-time imaging tool for industrial process analysis. A novel methodology is proposed to characterise the variation between consecutive ultrasonic data sets deriving from the ultrasonic hardware. The pulse-echo response corresponding to a planar back wall acoustic interface is used to infer the bandwidth, pulse length and sensitivity of each array element. This led to the development of a calibration methodology to enhance the accuracy of experimentally generated ultrasonic images. An algorithm enabling non-invasive through-steel imaging of an industrial process is demonstrated using a simulated data set. Using principal component analysis, signals corresponding to reverberations in the steel vessel wall are identified and deselected from the ultrasonic data set prior to image construction. This facilitates the quantification of process information from the image. An image processing and object tracking algorithm are presented to quantify the bubble size distribution (BSD) and bubble velocity from ultrasonic images. When tested under controlled dynamic conditions, the mean value of the BSD was predicted within 50% at 100 mms-1 and the velocity could be predicted within 30% at 100 mms-1. However, these algorithms were sensitive to the quality of the input image to represent the true bubble shape. The consolidation of these techniques demonstrates successful application of ultrasonic phased array imaging, both invasively and noninvasively, to a dynamic process stream. Key to industrial uptake of the technology are data throughput and processing, which currently limit its applicability to real-time process analysis, and low sensitivity for some non-invasive applications.Typical industrial process analysis techniques require an optical path to exist between the measurement sensor and the process to acquire data used to optimise and control an industrial process. Ultrasonic sensing is a well-established method to measure into optically opaque structures and highly focussed images can be generated using multiple element transducer arrays. In this Thesis, such arrays are explored as a real-time imaging tool for industrial process analysis. A novel methodology is proposed to characterise the variation between consecutive ultrasonic data sets deriving from the ultrasonic hardware. The pulse-echo response corresponding to a planar back wall acoustic interface is used to infer the bandwidth, pulse length and sensitivity of each array element. This led to the development of a calibration methodology to enhance the accuracy of experimentally generated ultrasonic images. An algorithm enabling non-invasive through-steel imaging of an industrial process is demonstrated using a simulated data set. Using principal component analysis, signals corresponding to reverberations in the steel vessel wall are identified and deselected from the ultrasonic data set prior to image construction. This facilitates the quantification of process information from the image. An image processing and object tracking algorithm are presented to quantify the bubble size distribution (BSD) and bubble velocity from ultrasonic images. When tested under controlled dynamic conditions, the mean value of the BSD was predicted within 50% at 100 mms-1 and the velocity could be predicted within 30% at 100 mms-1. However, these algorithms were sensitive to the quality of the input image to represent the true bubble shape. The consolidation of these techniques demonstrates successful application of ultrasonic phased array imaging, both invasively and noninvasively, to a dynamic process stream. Key to industrial uptake of the technology are data throughput and processing, which currently limit its applicability to real-time process analysis, and low sensitivity for some non-invasive applications

Relevance of polynomial matrix decompositions to broadband blind signal separation

The polynomial matrix EVD (PEVD) is an extension of the conventional eigenvalue decomposition (EVD) to polynomial matrices. The purpose of this article is to provide a review of the theoretical foundations of the PEVD and to highlight practical applications in the area of broadband blind source separation (BSS). Based on basic definitions of polynomial matrix terminology such as parahermitian and paraunitary matrices, strong decorrelation and spectral majorization, the PEVD and its theoretical foundations will be briefly outlined. The paper then focuses on the applicability of the PEVD and broadband subspace techniques — enabled by the diagonalization and spectral majorization capabilities of PEVD algorithms—to define broadband BSS solutions that generalise well-known narrowband techniques based on the EVD. This is achieved through the analysis of new results from three exemplar broadband BSS applications — underwater acoustics, radar clutter suppression, and domain-weighted broadband beamforming — and their comparison with classical broadband methods

Sonar systems for object recognition

The deep sea exploration and exploitation is one of the biggest challenges of the next century. Military, oil & gas, o shore wind farming, underwater mining, oceanography are some of the actors interested in this eld. The engineering and technical challenges to perform any tasks underwater are great but the most crucial element in any underwater systems has to be the sensors. In air numerous sensor systems have been developed: optic cameras, laser scanner or radar systems. Unfortunately electro magnetic waves propagate poorly in water, therefore acoustic sensors are a much preferred tool then optical ones. This thesis is dedicated to the study of the present and the future of acoustic sensors for detection, identi cation or survey. We will explore several sonar con gurations and designs and their corresponding models for target scattering. We will show that object echoes can contain essential information concerning its structure and/or composition

An Online Solution for Localisation, Tracking and Separation of Moving Speech Sources

The problem of separating a time varying number of speech sources in a room is difficult to solve. The challenge lies in estimating the number and the location of these speech sources. Furthermore, the tracked speech sources need to be separated. This thesis proposes a solution which utilises the Random Finite Set approach to estimate the number and location of these speech sources and subsequently separate the speech source mixture via time frequency masking