To overcome the limitation that traditional analog circuit fault diagnosis relies on single-modal data

a particle swarm wavelet convolution for cross-domain multimodal analog circuit fault diagnosis method is proposed.This method constructs a three-modal feature space covering the time

frequency

and time-frequency domains

adopts wavelet packet transform to extract refined time-frequency features and optimizes wavelet basis function parameters with the assistance of the particle swarm optimization(PSO) algorithm.A three-path hybrid encoder is designed

in which two parallel branches of one-dimensional convolutional neural network(1D-CNN) and two-dimensional convolutional neural network(2D-CNN) respectively process the original time-domain signal

frequency-domain signal converted by fast Fourier transform and wavelet time-frequency maps.An attention mechanism is introduced to realize cross-modal feature interaction.Meanwhile

a multi-scale residual structure is constructed by cascading dilated convolutions

residual connections and adaptive pooling layers to enhance the local feature extraction capability.