Browsing by Author "De Lange, Lydia"

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    Machine learning for antenna array failure analysis
    (Stellenbosch : Stellenbosch University, 2020-03) De Lange, Lydia; Ludick, D. J.; Grobler, Trienko Lups; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: This work investigated the use of machine learning to detect failed elements in an antenna array. The aim was to identify a trustworthy means of early detection and isolation of faulty elements to improve the reliability of measured data. Previous work has shown that it is theoretically possible to identify failed elements from the far-field radiation pattern, using machine-learning algorithms such as artificial neural networks and support vector models. However, literature seems void of studies that test howthe input data affects the accuracy of the machine-learning algorithm. It is possible to measure the far-field radiation pattern of earth-based antenna arrays, but very few researchers have validated their proposed techniques on a manufactured array. We therefore investigated the effects of various far-field sampling methods on the accuracy and training time of a feedforward neural network, and on the accuracies of different out-of-the-box classification algorithms, and the effect of the antenna array configuration on the accuracy of a support vector model. We simulated, manufactured and measured a 16-element circular patch antenna array to determine the feasibility of using the simulated far-field pattern as training data for a machine-learning algorithm designed to identify failures in a measured far-field pattern. We found it would not currently be feasible to employ machine learning to detect single element failures by measuring distortions in the far-field radiation patterns generated by a very large array of antennas in an irregular sparse configuration, such as those planned for the Square Kilometer Array (SKA) radio astronomy project.