Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach

Analog circuits are fundamental to a wide range of industrial systems, where their evaluation is essential for ensuring operational reliability and preventing system failures. However, diagnostic methodologies for analog circuits are markedly less developed than those for their digital counterparts,...

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Autor: Dieste Velasco, Mª Isabel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Recursos:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:dnet:riubu_______::8e82eab567185d3324c75fb1e709199d
Acesso em linha:https://hdl.handle.net/10259/11528
Access Level:acceso abierto
Palavra-chave:Machine learning
Evolutionary algorithm
RF
ANN
PSO
Simultaneous soft fault diagnosis
Fault classification
Electrónica analógica
Aprendizaje automático
Analog electronic systems
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spelling Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning ApproachDieste Velasco, Mª IsabelMachine learningEvolutionary algorithmRFANNPSOSimultaneous soft fault diagnosisFault classificationElectrónica analógicaAprendizaje automáticoAnalog electronic systemsMachine learningAnalog circuits are fundamental to a wide range of industrial systems, where their evaluation is essential for ensuring operational reliability and preventing system failures. However, diagnostic methodologies for analog circuits are markedly less developed than those for their digital counterparts, primarily due to the inherent difficulty of detecting soft faults within analog environments. One particularly challenging category of faults involves simultaneous degradations across multiple components that do not result in a hard failure of the circuit. Indeed, there is a notable lack of studies addressing the detection of simultaneous soft faults in analog circuits. This study proposes a method for identifying this type of soft fault occurrence in analog circuits by combining Machine Learning (ML) techniques, specifically Random Forests and Artificial Neural Networks, with an Evolutionary Algorithm (EA) based on Particle Swarm Optimization (PSO). The proposed approach is validated on a second-order Sallen-Key band-pass filter, a circuit in which soft fault classification is particularly challenging. Furthermore, the study highlights the performance improvements achieved through the proposed combined method in detecting and classifying simultaneous soft faults. This study demonstrates that an iterative process combining ML and EA techniques enables accurate fault prediction in electronic circuits. Moreover, the integration of these strategies can enhance the performance of classification problems that are traditionally addressed using either ML or EA in isolation. The effectiveness of the proposed method is evaluated using several statistical metrics, including the Matthews Correlation Coefficient (MCC), F1-score, and others.Open access funding provided by FEDER European Funds and the Junta de Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021-2027. Open access funding provided by the CRUE-CSIC agreement with Springer Nature, the FEDER European Funds, and the Junta de Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021–2027.Open access funding provided by FEDER European Funds and the Junta De Castilla y León under the Research and Innovation Strategy for Smart Specialization (RIS3) of Castilla y León 2021-2027Springer202620262026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10259/11528reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésCircuits, Systems, and Signal Processing. 2026Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:riubu_______::8e82eab567185d3324c75fb1e709199d2026-05-28T07:56:11Z
dc.title.none.fl_str_mv Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
title Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
spellingShingle Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
Dieste Velasco, Mª Isabel
Machine learning
Evolutionary algorithm
RF
ANN
PSO
Simultaneous soft fault diagnosis
Fault classification
Electrónica analógica
Aprendizaje automático
Analog electronic systems
Machine learning
title_short Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
title_full Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
title_fullStr Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
title_full_unstemmed Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
title_sort Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
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 Machine learning
Evolutionary algorithm
RF
ANN
PSO
Simultaneous soft fault diagnosis
Fault classification
Electrónica analógica
Aprendizaje automático
Analog electronic systems
Machine learning
topic Machine learning
Evolutionary algorithm
RF
ANN
PSO
Simultaneous soft fault diagnosis
Fault classification
Electrónica analógica
Aprendizaje automático
Analog electronic systems
Machine learning
description Analog circuits are fundamental to a wide range of industrial systems, where their evaluation is essential for ensuring operational reliability and preventing system failures. However, diagnostic methodologies for analog circuits are markedly less developed than those for their digital counterparts, primarily due to the inherent difficulty of detecting soft faults within analog environments. One particularly challenging category of faults involves simultaneous degradations across multiple components that do not result in a hard failure of the circuit. Indeed, there is a notable lack of studies addressing the detection of simultaneous soft faults in analog circuits. This study proposes a method for identifying this type of soft fault occurrence in analog circuits by combining Machine Learning (ML) techniques, specifically Random Forests and Artificial Neural Networks, with an Evolutionary Algorithm (EA) based on Particle Swarm Optimization (PSO). The proposed approach is validated on a second-order Sallen-Key band-pass filter, a circuit in which soft fault classification is particularly challenging. Furthermore, the study highlights the performance improvements achieved through the proposed combined method in detecting and classifying simultaneous soft faults. This study demonstrates that an iterative process combining ML and EA techniques enables accurate fault prediction in electronic circuits. Moreover, the integration of these strategies can enhance the performance of classification problems that are traditionally addressed using either ML or EA in isolation. The effectiveness of the proposed method is evaluated using several statistical metrics, including the Matthews Correlation Coefficient (MCC), F1-score, and others.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
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/10259/11528
url https://hdl.handle.net/10259/11528
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Circuits, Systems, and Signal Processing. 2026
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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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