2026-04-29T17:05:36Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/86822024-05-14T09:46:52Zcom_10259_4393com_10259_5086com_10259_2604col_10259_4394
Repositorio Institucional de la Universidad de Burgos
author
Dieste Velasco, Mª Isabel
2024-02-15T11:06:21Z
2024-02-15T11:06:21Z
2021-12
http://hdl.handle.net/10259/8682
10.3390/math9243247
2227-7390
In this study, machine learning techniques based on the development of a pattern–recognition neural network were used for fault diagnosis in an analog electronic circuit to detect the individual hard faults (open circuits and short circuits) that may arise in a circuit. The ability to determine faults in the circuit was analyzed through the availability of a small number of measurements in the circuit, as test points are generally not accessible for verifying the behavior of all the components of an electronic circuit. It was shown that, despite the existence of a small number of measurements in the circuit that characterize the existing faults, the network based on pattern-recognition functioned adequately for the detection and classification of the hard faults. In addition, once the neural network has been trained, it can be used to analyze the behavior of the circuit versus variations in its components, with a wider range than that used to develop the neural network, in order to analyze the ability of the ANN to predict situations different from those used to train the ANN and to extract valuable information that may explain the behavior of the circuit.
eng
Atribución 4.0 Internacional
Modeling
Analog circuits
Fault diagnosis
Neural networks
Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/8682/1/Dieste-Mathematics_2021.pdf
File
MD5
756811fa68caf736c38b94c86e6c15e1
14509714
application/pdf
Dieste-Mathematics_2021.pdf