2026-04-18T13:16:22Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/115282026-04-18T00:05:46Zcom_10259_4393com_10259_5086com_10259_2604col_10259_4394

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. 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. application/pdf eng Springer Circuits, Systems, and Signal Processing. 2026 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Machine learning Evolutionary algorithm RF ANN PSO Simultaneous soft fault diagnosis Fault classification Electrónica analógica Aprendizaje automático Analog electronic systems Machine learning Identification of Simultaneous Soft Faults in Analog Circuits Using a Hybrid PSO-Machine Learning Approach info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Circuits, Systems, and Signal Processing