Ada-CDDPM: A Novel AIGC Method for RF Front-End Fault Diagnosis Under Imbalanced Data

Data-driven fault diagnosis is a method widely used in the maintenance and support of radio frequency (RF) front-end. Its effectiveness depends on a large volume of high-quality training data. However, due to the low probability and uneven occurrence of faults, quantity of fault data is often less than the normal data. This data imbalanced problem greatly affects the accuracy of fault diagnosis for RF front-end. Data generation is an important way to address this problem, including generative adversarial networks (GAN), variational autoencoders (VAE), and denoising diffusion probabilistic models (DDPM). However, these methods suffer from issues such as unstable training, mode collapse, and uncontrollable generation. To solve these problems, this article proposed an adaptive conditional DDPM (Ada-CDDPM) based on improved U-Net3+, aiming to enable the model to better extract features of RF fault data and then guide it to generate the modal data required for RF system diagnosis. More specifically, this article proposed an adaptive CDDPM, which uses a conditional function with learnable coefficients vector to guide direction of training data generation. A differentiable loss function is proposed by this article to evaluate the quality of the generated training data. The learnable coefficient vector is optimized adaptively based on the evaluation results, enabling the final generated data to have good fidelity, diversity, and modal characteristics. This article constructed a simulation platform and an experimental platform to test the performance of the method. Comprehensive experiments show that data generated by the proposed method has significant advantages in diversity, fidelity, and is effective in solving RF front-end diagnosis problem under imbalanced data.