摘要
针对综采放顶煤开采过程中的放煤阶段,煤和矸石下落撞击刮板运输机产生的振动信号差异,采用小波变换研究振动信号的奇异性特征。根据信号奇异性特征,提出Fisher判别规则,用于识别放煤过程中煤或是矸石下落。将算法移植于硬件设备中,实验结果表明,该算法识别率高,抗干扰能力强,能够实时判断出下落物是煤还是矸石,可以实现动态的煤矸自动识别。该方法用于放煤工作面,可以即时做出对液压支架状态(支起或放下)的控制,代替现有人工识别操作方法,提高了放煤效率和回采率。
In order to distinguish rock from coal during caving, the singularity of the vibration signals of coal or rock hitting the armor plate is investigated using wavelet transform. Based on the characteristics of the singularity, the Fisher discriminant for distin- guishing rock from coal is proposed and the new equipment is developed. Experimental results show the proposed method has high recognition rate and strong anti-interference. The invented equipment can control timely what time the powered tail support should be up or down according to the identification results. The coal workers can be superseded by the equipment to improve the productivity and the mining rate.
出处
《计算机工程与设计》
CSCD
北大核心
2011年第5期1800-1803,共4页
Computer Engineering and Design
基金
河北省科学技术研究与发展指导计划基金项目(07213567)
关键词
煤矸识别
振动信号
小波变换
模系数极大法
奇异性检测
FISHER判别
distinguishing rock from coal
vibration signal
wavelet transform
modulus-maxima method
singularity detection
Fisher discriminant
