This paper introduces an innovative approach to enhance feature selection and enable dual fault diagnosis in analog circuits. By harnessing the combined power of fuzzy logic and neural networks, this study presents a robust framework for precisely pinpointing faults within the circuit. The operational integrity of any circuit hinges upon its inherent parameters, and the presence of defective components within the circuit distorts its performance, resulting in output deviations. The techniques elucidated in this research not only identify the specific faulty component but also quantify the extent of its deviation from the original parameters. A comparative analysis between the efficacy of fuzzy logic and neural networks in addressing this challenge is expounded upon. To demonstrate the practical application of these methodologies, a Sallen-key Bandpass filter is employed as the circuit under scrutiny (CUT). A comprehensive fault dictionary, constructed via the extraction of pertinent features from the CUT, serves as the cornerstone of this study. Notably, the fault table is meticulously generated, utilizing a step size of +/- 5% for parameter value perturbation.
