摘要
为了准确的预测采空区煤矿煤岩破裂与失稳前,岩石所释放出来的声发射信息的位置,并且根据山西焦煤的官地矿16403工作面获得的声发射事件的数据,因为该数据是一个非线性、高维的问题,提出了用PSO和SVM算法相结合的方法应用在煤岩声发射源定位中。以往的方法只是单纯的收集煤岩或岩石声发射信息,以至于定位出现失准、精度低和误差大的缺点。基于煤岩失稳前两者都会发出强烈的信号,文章提出了"1+1=1"的定位模型,既收集同一位置的岩石和煤岩体的声发射信号,由PSO-SVM分析处理后,得到其位置。仿真结果表明:应用PSO和SVM结合的方法进行煤岩声发射源定位的预测,在提高准确性和精确度的同时,也大大的提高了泛化的能力,该方法也大大减小定位失准的误差。
In order to predict accurately acoustic emission information location released by rock before coal rock fracture and instability, and the acoustic emission event data is a non-linear and high-dimensional problem gained from 16403 working zone in Guandi coal Shanxi Coking,The method of combining PSO and SVM algorithm used in the positioning of coal and rock acoustic emission in this paper. The traditional method has a disadvantage of misalignment ,low accuracy and big error, because it simply collect coal rock or rock acoustic emission information. According to strong signal sent by the two, the paper proposes a＂ 1 ＋ 1 = 1 ＂positioning method, in other words, the location is confirmed by collecting the rock and coal-rock acoustic emission information processed by PSO-SVM in the same location. The simulation results show that this method improves accuracy and precision, at the same time, the generalization ability is improved greatly, it can also greatly reduce inaccurate positioning error.
出处
《传感技术学报》
CAS
CSCD
北大核心
2013年第3期402-406,共5页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金项目(51274118
70971059)
辽宁省科技攻关基金项目(2011229011)
辽宁省教育厅基金项目(L2012119)
教育部重点实验室项目
关键词
粒子群优化
支持向量机
声发射
源定位
particle swarm optimization
support vector machine
acoustic emission
source location
