煤炭工程 ›› 2015, Vol. 47 ›› Issue (6): 119-122.doi: 10.11799/ce201506038

• 研究探讨 • 上一篇    下一篇

基于声压信号时域特征的综放工作面煤岩性状识别方法研究

薛光辉,柳二猛,赵新赢,胡保华,丁伟健   

  1. 中国矿业大学(北京)
  • 收稿日期:2014-07-30 修回日期:2014-10-10 出版日期:2015-06-10 发布日期:2015-06-12
  • 通讯作者: 柳二猛 E-mail:jiating0019liu@163.com

Research of coal-rock character recognition in fully mechanized caving faces based on acoustic pressure data time domain analysis

  • Received:2014-07-30 Revised:2014-10-10 Online:2015-06-10 Published:2015-06-12

摘要: 针对综放工作面煤岩性状识别的技术问题,利用YHJ(C) 矿用便携式测振记录仪开展煤矿井下综放工作面顶煤放落实验,采集到大量一手试验数据;然后,对选用的声压数据进行信号预处理以消除趋势项;最后,对预处理后的声压数据进行时域特征分析,得到不同工况下后尾梁声压信号的时域特征指标。对比发现,峰峰值、方差以及峭度指标对工况敏感,且方差对煤岩性状具有更高的识别率,据此提出一种以方差作为识别指标的基于声压信号时域特征的分析方法,为提高综放工作面放顶煤的自动化、智能化提供技术支持。

关键词: 综放工作面, 煤岩性状识别, 声压信号, 时域特征, 放顶煤工艺

Abstract: For the technical problems of coal and rock character recognition in fully mechanized caving faces. A method on characterization and recognition of coal and rock traits were discussed based on the time domain indexes of acoustic pressure data according to the differences of physics and mechanical parameters of coal and rock ,and the differences of acoustic pressure data when coal and rock falling impact the rear beam of the sublevel caving hydraulic support. Firstly, the top coal caving experiments were carried out with mining portable vibration recorder developed by China University of Mining and Technology(Beijing)in fully mechanized caving faces in the underground mines, and the acoustic pressure data in quantity were acquired; Then, signal preprocessing were carried on to remove trend items for the selected sound pressure data ; Finally, the acoustic pressure dates were analyzed in time domain and the time domain features were acquired. Comparison found, peak to peak, variance and kurtosis index are sensitive to the working conditions and the variance with a higher recognition rate. Accordingly proposed an analytical method that based on time-domain features of sound pressure date which used variance as recognition indicator, providing technical support for improving the caving automation and intelligent in the fully mechanized caving face.

Key words:     fully mechanized caving faces, coal and rock character recognition, acoustic pressure data, time domain feature, top caving technology

中图分类号: