Physics-Informed Fourier Prior-Guided DDPM: Data Generation for Enhancing Fault Diagnosis

Abstract

Due to the scarcity of industrial fault data, deep learning-based fault diagnosis models often struggle to fully learn fault features under few-shot conditions, leading to degraded diagnostic performance. To address this issue, this article, for the first time, proposes a physics-informed Fourier prior-guided denoising diffusion probabilistic model (FPG-DDPM) for fault sample generation. The proposed model introduces theoretical fault characteristic frequencies and fault labels as dual-condition inputs, enabling the generated samples to exhibit clear class discriminability and fault-related frequency characteristics. In terms of model structure, a Mamba module is introduced to enhance the capture of global features and long-range contextual dependencies in time–frequency representations. Meanwhile, Fourier prior guidance is introduced during the reverse denoising process to dynamically constrain and correct the noise predicted by the model, thereby improving the accuracy and physical plausibility of the generated samples. To evaluate the effectiveness and physical plausibility of the generated data in downstream diagnosis tasks, layer-wise relevance propagation (LRP) is introduced. By comparing the classification decision basis of real and generated samples in the CNN diagnostic model, LRP provides interpretable validation for the evaluation of generated data. Experimental results on the Case Western Reserve University (CWRU) and Southeast University (SEU) datasets show that fault diagnosis models trained with augmented datasets achieve accuracies of 99.96% and 98.82%, respectively, demonstrating that the proposed method can improve fault diagnosis performance under few-shot learning conditions.