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.
