A Multisource Information Fusion Anomaly Detection Framework Based on Denoising Diffusion Probabilistic Model for Rotating Machinery

Rotating machinery fault detection is critically important for industrial systems. However, current methods relying on single-modal signals often yield incomplete feature representation and limited robustness. Denoising diffusion probabilistic models (DDPMs) offer promise for anomaly detection due to their strong generative capabilities and ability to train solely on healthy data. To overcome these limitations, this paper introduces MFE-DDPM, a novel rotating machinery anomaly detection method integrating multimodal feature fusion with an attention-enhanced DDPM. Our approach significantly enhances detection accuracy and robustness. First, vibration and acoustic signals are converted into time-frequency spectrograms via time-frequency analysis and a hyperparameter-free encoding method, while temperature data are transformed into grayscale images. These modalities are fused at the data level by mapping them to the R, G, and B channels of an RGB image, respectively. Subsequently, the attention-enhanced DDPM detects anomalies based on the reconstruction error of samples generated from the fused input. Validation on the Ottawa Multimodal Bearing Dataset shows that MFE-DDPM achieves a highest anomaly detection accuracy of 99.50%, with an average accuracy of 98.65% over ten repeated trials, outperforming both conventional single- and dual-modal methods and multimodal approaches without attention mechanisms.