Element failure detection in linear antenna arrays using case-based reasoning

Abstract

The present work proposes a novel case-based reasoning system for fault diagnosis in moderate or large linear antenna arrays. This system identifies the set of elements that are most likely to be defective, helping to significantly reduce the computational costs of their detection (e.g., using an optimization technique such as a genetic algorithm).

Joint Azimuth and Elevation Angle Estimation Using Incomplete Data Generated by a Faculty Antenna Array

“…The studied MCMP algorithm is summarized in Table. 2 We assume that a few number of elements at random locations have failed to produce to the output. There are techniques available for knowing the number of elements failed and their locations and can be found in [23]. Three examples are considered to evaluate the performance.…”

A Survey on Applications of Neural Networks and Genetic Algorithms in Fault Diagnostics for Antenna Arrays

“…Authors in [26] have extended the idea further to present a flexible approach of locating fault elements in antenna arrays using artificial neural networks employing MLPs trained in back propagation. Waveguide fed longitudinal slot array antennas: Fault diagnosis using measurements of input impedance [23] Genetic algorithm 12-slot linear array 5 Antenna array adjust with adaptive neuronal system [24] Adaline neural network 16-element linear array 6 Finding failed element positions in linear Antenna arrays using neural networks [25] Multi layer perceptron neural network 5-element array 7 An ANN application for fault finding in Antenna arrays [26] Multi layer perceptron neural network 16-element linear micro strip array, d=0.3λ 8 A comparison among several techniques for finding defective elements in antenna array [27] Case based reasoning, Neural networks, Genetic algorithm 100-element linear array, d=0.5λ 9 Element failure detection in linear antenna array using case based reasoning [28] Case based reasoning, SOM neural network 100-element linear array, d=0.5λ 10 A genetically trained neural network application for fault finding in antenna arrays [29] Multi layer perceptron with Genetic algorithm 16-element linear array, d=0.3λ…”

“…Authors in [28] have proposed a case based reasoning system for fault diagnosis for moderate or large linear antenna arrays and also self organizing map (SOM) to retrieve the cases most similar to the radiation pattern being analysed. This method has been found to be more effective with a success rate of around 93% as compared to 76.7 % for MLP 76.0 % for RBF.…”

Projections of limiting states for load-bearing structures of reflectors made of polymer composites

“…This concerns reflectors that are both standalone technical objects [1,2] and parts of antenna arrays [3]. Commonly, in the capacity of the failure criterion, one uses the mean square deviation of the reflecting surface.…”