Abstract

Accurate temperature measurement is vital for assessing the reaction atmosphere of industrial rotary kilns. However, dynamic water mist interference imposes significant challenges to accurate infrared temperature measurement. To this end, this study proposes a temperature measurement compensation method based on infrared multi-feature fusion. First, we developed an industrial system to collect infrared data from the kiln. To address the issue of unclear water mist in infrared images caused by its low resolution and weak texture features, we designed an artificial feature based on infrared images and temperature differences named water mist level (WML). This feature effectively represents the size of the water mist in the image. Additionally, a classification model is established to identify the WML automatically. Subsequently, a novel multi-scale feature fusion network called efficient regression feature pyramid network (ERFPN) is proposed to acquire multi-scale image features. Finally, we propose a grouped feature fusion network (GFFN) to fuse multi-scale image features, WML, and interfered temperature. Experimental results show that the proposed compensation method achieves satisfactory infrared temperature measurement results and significantly reduces the temperature measurement error caused by water mist.

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