摘要
针对当前煤矿安全生产对瓦斯浓度监测的需要,采用近红外光谱分析技术结合先进的Zigbee无线通信技术,设计了一款煤矿瓦斯无线实时智能监控系统。在算法上将小波理论与神经网络算法相融合,建立的近红外瓦斯浓度预测模型具有更高的检测精度和系统稳定性。在硬件上采用Xilinx公司的FPGA芯片对系统进行硬件实现,最终完成对系统工作性能的测试。系统的检测最大误差不超过0.2%,证明系统的工作性能可以达到采矿过程的需要,能够有效完成对煤矿瓦斯气体浓度的实时监测目的,为能够实时监测矿井内瓦斯浓度信息提供了一种便捷的传感技术。
In view of the current coal mine safety and production the needs for gas concentration monitoring,using near infrared spectral analysis technology,combined with advanced Zigbee wireless communication technologies. In al- gorithm combining wavelet theory and neural network algorithm,establish the near infrared gas concentration predic- tion model has higher detection accuracy and system stability. And using Xilinx FPGA chip system for hardware im- plementation of the company. Finished work on the system performance testing,system testing maximum error less than 0.2% ,prove the system performance can reach the needs of the mining process,can effectively complete the purpose of the real-time monitoring for coal mine gas concentration.
出处
《自动化与仪表》
北大核心
2014年第9期45-48,共4页
Automation & Instrumentation
基金
山西省教育厅科技项目(20110007)
关键词
小波
神经网络
瓦斯
近红外光
传感器
wavelet
neural network
gas
near infrared （NIR）
sensor
