Soft fault diagnosis in analog electronic circuits using supervised machine learning

Analog circuits are commonly used in a wide range of industrial applications, and their assessment is of great importance to ensure proper functionality and prevent faults. However, this task is not as fully developed and is significantly less advanced compared to the assessment of digital circuits,...

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Detalles Bibliográficos
Autor: Dieste Velasco, Mª Isabel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/10838
Acceso en línea:https://hdl.handle.net/10259/10838
Access Level:acceso abierto
Palabra clave:Soft fault diagnosis
Fault classification
Machine-learning
Electronic circuits
Electrónica analógica
Analog electronic systems
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spelling Soft fault diagnosis in analog electronic circuits using supervised machine learningDieste Velasco, Mª IsabelSoft fault diagnosisFault classificationMachine-learningElectronic circuitsElectrónica analógicaAnalog electronic systemsAnalog circuits are commonly used in a wide range of industrial applications, and their assessment is of great importance to ensure proper functionality and prevent faults. However, this task is not as fully developed and is significantly less advanced compared to the assessment of digital circuits, as soft faults are particularly difficult to detect in analog circuits. This study addresses the application of supervised classification techniques for the detection and classification of soft faults in analog circuits. A feature extraction methodology is proposed based on voltage measurements at key circuit points and across different frequencies, enabling precise characterization of system behavior. From this feature, a benchmark employing different machine learning methods was used. The evaluated classifiers include k-Nearest Neighbors (KNN), Naïve Bayes (NB), Discriminant Analysis Classifier (DAC), Classification Decision Tree (CDT), Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Each model was optimized through hyperparameter tuning and validated using cross-validation techniques. The results indicate that ANN and SVM achieved the best performance, attaining an accuracy of 97.92 % and 97.22 % on test data, with a global Matthews Correlation Coefficient (MCC) of 97.76 % and 97.01 %, respectively. Although RF obtained the highest training accuracy (99.39 %), its performance significantly dropped during testing (93.06 %, MCC of 92.52 %), indicating overfitting. Additionally, models such as KNN and DAC demonstrated solid performance, whereas NB and CDT were the least effective. These findings highlight the importance of carefully selecting both the feature set and the classification model for fault detection in electronic circuits. A Sallen-Key band-pass filter was used as the circuit under test (CUT), as soft fault classification in this type of circuit is particularly challenging. This study demonstrates that it is possible to accurately predict faults in circuits similar to the one analyzed.Elsevier202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10259/10838reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésIntegration. 2025, V. 104, 102482https://doi.org/10.1016/j.vlsi.2025.102482Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/108382026-05-28T07:56:11Z
dc.title.none.fl_str_mv Soft fault diagnosis in analog electronic circuits using supervised machine learning
title Soft fault diagnosis in analog electronic circuits using supervised machine learning
spellingShingle Soft fault diagnosis in analog electronic circuits using supervised machine learning
Dieste Velasco, Mª Isabel
Soft fault diagnosis
Fault classification
Machine-learning
Electronic circuits
Electrónica analógica
Analog electronic systems
title_short Soft fault diagnosis in analog electronic circuits using supervised machine learning
title_full Soft fault diagnosis in analog electronic circuits using supervised machine learning
title_fullStr Soft fault diagnosis in analog electronic circuits using supervised machine learning
title_full_unstemmed Soft fault diagnosis in analog electronic circuits using supervised machine learning
title_sort Soft fault diagnosis in analog electronic circuits using supervised machine learning
dc.creator.none.fl_str_mv Dieste Velasco, Mª Isabel
author Dieste Velasco, Mª Isabel
author_facet Dieste Velasco, Mª Isabel
author_role author
dc.subject.none.fl_str_mv Soft fault diagnosis
Fault classification
Machine-learning
Electronic circuits
Electrónica analógica
Analog electronic systems
topic Soft fault diagnosis
Fault classification
Machine-learning
Electronic circuits
Electrónica analógica
Analog electronic systems
description Analog circuits are commonly used in a wide range of industrial applications, and their assessment is of great importance to ensure proper functionality and prevent faults. However, this task is not as fully developed and is significantly less advanced compared to the assessment of digital circuits, as soft faults are particularly difficult to detect in analog circuits. This study addresses the application of supervised classification techniques for the detection and classification of soft faults in analog circuits. A feature extraction methodology is proposed based on voltage measurements at key circuit points and across different frequencies, enabling precise characterization of system behavior. From this feature, a benchmark employing different machine learning methods was used. The evaluated classifiers include k-Nearest Neighbors (KNN), Naïve Bayes (NB), Discriminant Analysis Classifier (DAC), Classification Decision Tree (CDT), Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Each model was optimized through hyperparameter tuning and validated using cross-validation techniques. The results indicate that ANN and SVM achieved the best performance, attaining an accuracy of 97.92 % and 97.22 % on test data, with a global Matthews Correlation Coefficient (MCC) of 97.76 % and 97.01 %, respectively. Although RF obtained the highest training accuracy (99.39 %), its performance significantly dropped during testing (93.06 %, MCC of 92.52 %), indicating overfitting. Additionally, models such as KNN and DAC demonstrated solid performance, whereas NB and CDT were the least effective. These findings highlight the importance of carefully selecting both the feature set and the classification model for fault detection in electronic circuits. A Sallen-Key band-pass filter was used as the circuit under test (CUT), as soft fault classification in this type of circuit is particularly challenging. This study demonstrates that it is possible to accurately predict faults in circuits similar to the one analyzed.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10259/10838
url https://hdl.handle.net/10259/10838
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Integration. 2025, V. 104, 102482
https://doi.org/10.1016/j.vlsi.2025.102482
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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