Thermal Fault Detection of High-Speed Direct-Driven Blower Components Using Thermal-Visible Image Fusion and Semantic Segmentation

A thermal fault detection method for high-speed direct-driven blower components is proposed, using thermal and visible image fusion along with semantic segmentation. The proposed method follows three steps: multimodal image fusion, component semantic segmentation of the fused image, and temperature level segmentation. First, an end-to-end image fusion network based on an improved denoising diffusion model is used, a perceptually prioritized weighted loss is introduced for training, and an alternate training strategy is used to improve the quality of the fused images. In the second step, a lightweight segmentation network is proposed to reduce the model size and inference time while improving the segmentation accuracy. Finally, thermal images are processed by clustering methods. Experiments on real industrial objects show that the proposed method composed of infrared and optical image fusion, semantic segmentation, and temperature clustering networks improves significantly the fault temperature detection on different blower components.