RT info:eu-repo/semantics/article
T1 Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach
A1 Dieste Velasco, Mª Isabel
K1 Machine learning
K1 Evolutionary algorithm
K1 RF
K1 ANN
K1 PSO
K1 Simultaneous soft fault diagnosis
K1 Fault classification
K1 Electrónica analógica
K1 Analog electronic systems
K1 Aprendizaje automático
K1 Machine learning
AB 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.
PB Springer
SN 0278-081X
YR 2026
FD 2026-03
LK https://hdl.handle.net/10259/11528
UL https://hdl.handle.net/10259/11528
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 18-abr-2026