Industrial Data Synthesis Framework for Fault Diagnosis Using Diffusion and Flow-Based Models

Fault diagnosis plays a significant role in the intelligent maintenance of industrial processes. Although there are numerous deep learning methods designed for fault diagnosis, certain challenges remain difficult to address in real-world industrial scenarios. These challenges include ensuring high- precision fault detection while maintaining industrial privacy in the presence of sensitive data, as well as achieving accurate detection when fault samples are limited. This paper proposes an industrial data generation framework using diffusion and flow- based models. The proposed approach learns the distribution of the original data to generate synthetic samples with similar characteristics, effectively resolving the dual challenges of data sensitivity and scarcity. Experiments on the Tennessee Eastman dataset demonstrate that our approach outperforms existing methods in fault diagnosis performance.