Intelligent Sensing System Achieves 95% Accuracy in High-Voltage Cable Monitoring for Coal Mines
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The safe and reliable operation of coal mines depends critically on the condition of high-voltage power cables, and a new monitoring system promises to significantly improve their performance. Z Gao, J Li, and L Tao, working with B Meng, have developed an intelligent sensing technology that provides real-time assessment of cable health. This system utilises high-frequency current sensors and distributed fibre optic temperature sensors to detect early signs of insulation defects and overheating, offering a proactive approach to preventing power outages. The research demonstrates a diagnostic accuracy exceeding 95%, representing a substantial advancement in mine safety and power supply reliability by enabling timely intervention before failures occur. This work focuses on developing a system capable of detecting potential faults and degradation before they cause operational disruptions or safety hazards. The approach implements intelligent sensors and data analysis techniques to provide real-time insights into cable performance and condition, ultimately enhancing operational efficiency and safety protocols within coal mines.
Mine Cable Monitoring via Distributed Sensing This study introduces a three-layer architecture for online high-voltage cable monitoring, specifically designed for the challenging environment of coal mines. Researchers engineered a field data acquisition layer comprising high-frequency current sensors and distributed optical fiber temperature sensors deployed along cable routes. These current sensors reliably detect partial discharge signals, indicators of insulation defects, with a sensitivity exceeding 2 picocoulombs and operating between 30kHz and 20MHz. Simultaneously, distributed optical fiber temperature sensors continuously monitor temperature distribution along the entire cable length, achieving a temperature measurement accuracy of ±1°C with 1-meter spatial resolution. The study advances beyond simple data collection by employing adaptive filtering techniques, such as wavelet transform denoising, to isolate weak partial discharge pulses and subtle temperature anomalies from background noise. Algorithms incorporate temperature compensation models to correct for variations in sensor readings, ensuring accurate data despite environmental fluctuations. To further enhance reliability, the team implemented data fusion algorithms, including Kalman filtering and machine learning-based regression models, integrating multi-sensor data such as temperature, current load, and vibration. This cross-validation process identifies spurious readings and provides a robust assessment of cable operational state, facilitating a transition from periodic maintenance to predictive and condition-based maintenance strategies. This comprehensive approach effectively prevents catastrophic failures, such as electrical fires caused by progressive insulation breakdown, in a critical industrial setting. The system was tested in a small-scale mine with an annual production capacity of 600,000 tons, monitoring two 10kV high-voltage cable circuits spanning approximately 4. 2 kilometers. High-Voltage Cable Monitoring with Intelligent Sensing This work details a breakthrough in high-voltage cable monitoring for coal mines, employing intelligent sensing technology to enhance operational safety and reliability. Scientists designed a three-layer architecture system that accurately acquires real-time data on partial discharge signals and temperature distribution. The system utilizes high-frequency current sensors and distributed optical fiber temperature sensors to continuously monitor cable conditions within the challenging underground environment. Experiments demonstrate the system achieves diagnostic accuracy exceeding 95% in identifying cable insulation defects and potential overheating hazards. The research team measured temperature profiles with a typical accuracy of ±1°C and spatial resolution up to 1 meter using distributed optical fiber temperature sensing. The system triggers multi-level alerts when localized cable temperature exceeds a predefined threshold of 80°C, enabling proactive intervention before thermal runaway occurs. Furthermore, advanced multi-parameter fiber optic sensors simultaneously measure vibration, acoustic emissions, and mechanical strain, providing a comprehensive view of cable health. Data shows the system can detect partial discharge activity and physical impacts, while distributed strain sensing identifies mechanical deformation or external pressure changes. Scientists integrated low-power wide-area network communication modules, such as LoRa or NB-IoT, to form a resilient wireless data acquisition network. This network supports real-time transmission of structured data to cloud-based analytics platforms, establishing a robust foundation for intelligent condition monitoring. High-frequency current transformers capture transient pulse signals generated by internal discharges, analyzing parameters such as magnitude, phase, and repetition rate. Measurements reveal pulse sequences from persistent surface discharges at cable terminations range from 100 to 500 pC, exhibiting a specific phase relationship with the power frequency voltage. The system’s ability to detect temperature rises exceeding 5°C per minute across adjacent sensing points enables immediate alerts for potential overheating events.
Mine Cable Faults Diagnosed With High Accuracy This research demonstrates a novel high-voltage cable monitoring system designed for use in coal mines, achieving a diagnostic accuracy exceeding 95% in identifying cable insulation defects and potential overheating hazards. By integrating high-frequency current sensors and distributed fiber temperature sensors, the system provides real-time data acquisition and employs a combined edge and cloud computing approach for data analysis and fault diagnosis. The successful implementation of this system in a working mine directly addresses common cable failure modes and contributes to improved reliability of power supply, reduced unplanned production stoppages, and extended equipment service life. The study also highlights the importance of practical considerations for deployment, including ensuring intrinsic safety, electromagnetic compatibility, and proper grounding of monitoring devices. While the system demonstrates significant advancements in diagnostic capabilities and supports dynamic optimization of cable load management, the authors acknowledge ongoing technical challenges related to resilience against electromagnetic interference within complex mine environments. Future work will focus on incorporating adaptive sensor calibration mechanisms and artificial intelligence-driven predictive models to further enhance the system’s intelligence, adaptability, and scalability, potentially broadening its application across various industrial power supply scenarios and contributing to the development of fully autonomous mining electrical infrastructures. 👉 More information 🗞 Research on a Monitoring System for High-Voltage Cables in a Coal Mine Based on Intelligent Sensing Technology 🧠 ArXiv: https://arxiv.org/abs/2512.07714 Tags: