Fiber Optic Sensors for Coal Mine Hazard Detection
A number of health and safety hazards present in underground coal mines, which include methane gas explosion, coal combustion, rock roof collapse, and flooding. Methane gas and coal combustion have been recognized by the coal mine industry as two major hazards, which resulted in most of the heavy casualties and economic losses. Conventional catalytic methane gas sensors suffer from poor accuracy and cumbersome maintenance, which is the bottleneck of methane hazard prevention. Coal mine combustion monitoring has been relying on gas tubing bundles system, which suffers from long-time delay and poor reliability. Semiconductor laser diode methane gas sensors have been developed which have the advantages of low-power consumption, 0–100% full detection range, high accuracy, and no need of recalibration. Fiber optic Raman distributed sensors have been deployed in coal mine goaf and successfully detected combustion hazard in early phase. The FOS-based mine hazard detection system offers unique advantages of intrinsic safety, multi-location and multi-parameter monitoring. The application of FOS on monitoring of methane, coal combustion, micro-seismic, equipment condition, and rescue information systems is discussed in this chapter, showing future trend of research in this area.
KeywordsFiber optic sensor Coal mine Methane Seismic Equipment condition monitoring Rescue Hazard detection
The work presented here was supported by funding from Chinese Ministry of Science and Technologies, Department of Science & technology, Shandong Province, national grants for international collaboration centre on OFS and IOT for safety, as well as funding support by Shandong Academy of Science, China. The information presented here have been supported by the colleagues at Laser Institute, Shandong Academy of Science and Micro-Sensor Photonics Ltd., as well as Yankuang Coal Mine Group and many industrial partners. Special thanks to Yubin Wei, Guangdong Song, Yanfang Li, Guangxian Jin, Jie Hu, Binxin Hu, Jinyu Wang, Jiqiang Wang, Tingting Zhang, Weisong Zhao, Lin Zhao, Chengxiang Song, Chang Wang, Jiasheng Ni, Zhaowei Wang, Guofeng Dong, Junpeng Ma, Xiangjun Meng, and Zengyu Zhao for their contribution and support in this work.
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