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

Xusheng Xue, Jianxin Yang (), Hongkui Zhang, Yuan Tian, Qinghua Mao, Enqiao Zhang and Fandong Chen
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Xusheng Xue: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Jianxin Yang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Hongkui Zhang: CCTEG Shenyang Research Institute, Shenyang 113122, China
Yuan Tian: CCTEG Intelligent Storage Technology Co., Ltd., Beijing 100013, China
Qinghua Mao: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Enqiao Zhang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Fandong Chen: CCTEG Shenyang Research Institute, Shenyang 113122, China

Energies, 2025, vol. 18, issue 19, 1-23

Abstract: 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.

Keywords: coal mine electrical equipment; temperature rise monitoring; infrared measurement; support vector regression; fusion prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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