2026-04-18T13:30:01Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/115282026-04-18T00:05:46Zcom_10259_4393com_10259_5086com_10259_2604col_10259_4394
Repositorio Institucional de la Universidad de Burgos
author
Dieste Velasco, Mª Isabel
2026-04-17T12:49:15Z
2026-04-17T12:49:15Z
2026-03
0278-081X
https://hdl.handle.net/10259/11528
10.1007/s00034-026-03531-4
1531-5878
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.
eng
Atribución 4.0 Internacional
Machine learning
Evolutionary algorithm
RF
ANN
PSO
Simultaneous soft fault diagnosis
Fault classification
Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
info:eu-repo/semantics/article
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
URL
https://riubu.ubu.es/bitstream/10259/11528/1/Dieste-cssp_2026.pdf
File
MD5
a1cf56f8b6b023e893ca97d889b97bce
3064234
application/pdf
Dieste-cssp_2026.pdf