A Hybrid Fault Diagnosis Framework Based on Test Stimulus Optimization and MPCNN for Analog Circuits

Hybrid fault diagnosis in analogue circuits remains a challenging task, particularly due to the overlapping characteristics of different component faults, which often degrade the discriminative power of conventional diagnostic features. To overcome this limitation, we propose a novel diagnosis framework that integrates optimized test stimulus generation with a multi-period convolutional neural network (MPCNN) for accurate and robust fault identification. In the first stage, a bio-inspired optimization algorithm selects test stimuli that amplify the differences between normal and faulty circuit responses in both amplitude-frequency and phase-frequency domains. In the second stage, the MPCNN adaptively learns discriminative temporal-spatial features from the optimized responses, while a calibrated cross-entropy loss function improves classification confidence. The proposed approach is evaluated on multiple benchmark analogue circuits, where the diagnostic accuracy rates reached 99.62%, 98.27% and 91.46% respectively on the three standard circuits. The experimental results reveal statistically significant improvements in diagnostic accuracy, enhanced noise robustness, and superior generalization performance compared to state-of-the-art methods.