Cross-Media Infrared Measurement and Temperature Rise Characteristic Analysis of Coal Mine Electrical Equipment

With the advancement of coal mine electrical equipment toward larger scale, higher complexity, and greater intelligence, traditional temperature rise monitoring methods have revealed critical limitations such as intrusive measurement, low spatial resolution, and delayed response. This study proposes a novel cross-media infrared measurement method combined with temperature rise characteristic analysis to overcome these challenges. First, a cross-media measurement principle is introduced, which uses the enclosure surface temperature as a proxy for the internal heat source temperature, thereby enabling non-invasive internal temperature rise measurement. Second, a non-contact, infrared thermography-based array-sensing measurement approach is adopted, facilitating the transition from traditional single-point temperature measurement to full-field thermal mapping with high spatial resolution. Furthermore, a multi-source data perception method is established by integrating infrared thermography with real-time operating current and ambient temperature, significantly enhancing the comprehensiveness and timeliness of thermal state monitoring. A hybrid prediction model combining Support Vector Regression (SVR) and Random Forest Regression (RFR) is developed, which effectively improves the prediction accuracy of the maximum internal temperature—particularly addressing the issue of weak surface temperature features during low heating stages. The experimental results demonstrate that the proposed method achieves high accuracy and stability across varying operating currents, with a root mean square error of 0.741 °C, a mean absolute error of 0.464 °C, and a mean absolute percentage error of 0.802%. This work provides an effective non-contact solution for real-time temperature rise monitoring and risk prevention in coal mine electrical equipment.