Toward Multimodal Graph Sequence Generation: A Denoising Diffusion Approach for Wheeled Robot Fault Diagnosis

Abstract

Wheeled robots play a crucial role in Industrial Internet of Things (IIoT)-enabled manufacturing environments, and ensuring their reliable operation is essential for production efficiency and safety. However, their inherent complexity makes them prone to faults, while limited fault data results in imbalanced datasets, posing challenges for deep-learning-based fault diagnosis models. Existing denoising diffusion probabilistic model (DDPM)-based fault diagnosis methods tend to address single-channel scenarios, ignoring the graph relationships inherent in multichannel sensor data. Furthermore, current graph-based DDPM models also struggle in wheeled robot scenarios due to its multimodal nature. To address these challenges, we propose an enhanced DDPM-based method for imbalanced fault diagnosis of wheeled robots. Our method integrates graph operations into the noise prediction network of the DDPM framework, enabling efficient modeling of the complex spatial–temporal relations in multimodal graphical sequence data via a newly designed spatial–temporal graph U-Net (STGU-Net). Additionally, we present a dynamic degradation mechanism for the prior graph, simulating gradual structural changes during the diffusion process. Extensive experiments on a real-world wheeled robot platform demonstrate the superiority of the proposed model over state-of-the-art methods from multiple perspectives, showcasing its effectiveness in generating high-quality data and mitigating the imbalance problem in wheeled robot fault diagnosis.