An Interpretable Latent Denoising Diffusion Probabilistic Model for Fault Diagnosis under Limited Data

Despite the remarkable success of end-to-end intelligent diagnosis methods, the shortage of available training data remains one of the most challenging issues in real industrial scenarios. In light of this, a wide variety of deep generative models are developed for data volume expansion. Notably, the denoising diffusion probabilistic model (DDPM) has recently shown impressive sample quality and diversity in various tasks. However, DDPM typically operates in the original pixel space, resulting in an expensive computational cost and restricting its applicability in industrial applications. In tackling the above issues, we develop an interpretable vector quantization-guided latent denoising diffusion probability model (IVQ-LDM) in this work. In IVQ-LDM, the vector quantized-variational autoencoder is introduced to compress the data to a lower dimensional space, where the kernels with physical meaning are then designed in the first layer to enhance the density of latent information and improve model interpretability. After that, a conditional DDPM is built in this latent space to learn the low-dimensional representation for data augmentation. Compared with existing methods, the IVQ-LDM achieves enhancements in sample quality, computational efficiency, and interpretability. Extensive experiments on three mechanical systems corroborate the effectiveness and superiority of the proposed method.