RT info:eu-repo/semantics/article
T1 Fault detection in analog electronic circuits using fuzzy inference systems and particle swarm optimization
A1 Dieste Velasco, Mª Isabel
K1 Analog electronic circuits
K1 Fault diagnosis
K1 Fault classification
K1 Fuzzy inference systems (FIS)
K1 Particle swarm optimization (PSO)
K1 Pattern search
K1 Electrotecnia
K1 Electrical engineering
AB Fault detection in analog circuits is of great importance to predict the correct operation of the circuit. For this purpose, soft computing techniques such as those based on the application of fuzzy inference systems stand out. However, given the large variability that can exist in analog circuits due to component tolerance, the initial fuzzy inference system (FIS) may not be able to accurately diagnose the different hard faults. This study presents a methodology to diagnose and detect the faults that can occur in analog circuits, which is based on the development of a FIS, starting from a specific fault situation in the analog circuit, and subsequently on the optimization of the membership functions using an evolutionary algorithm so that the adjusted FIS can classify and predict different failure situations. To this end, the application of optimization techniques based on particle swarm optimization (PSO) will be analyzed to develop a FIS capable of predicting different faults. In addition, pattern search algorithm will also be analyzed. A Sallen-Key band-pass filter and a single stage of a small-signal amplifier are used as test circuits. The proposed methodology shows that it is possible to accurately predict the faults that could arise in the circuits under study.
PB Elsevier
SN 1110-0168
YR 2024
FD 2024-05
LK http://hdl.handle.net/10259/9304
UL http://hdl.handle.net/10259/9304
LA eng
DS Repositorio Institucional de la Universidad de Burgos
RD 28-jun-2024