摘要
研究基于信息融合技术的采煤机煤岩识别技术,使用多个传感器代替传统单个传感器建立煤岩识别系统,并以模糊神经网络算法作为系统的核心算法,从而提高采煤机煤岩识别的稳定性、抗干扰能力以及准确性等。使用采煤机滚筒截割煤壁时振动、阻力矩以及电机电流等进行监测,并采集数据提取特征值,通过模糊神经网络算法进行网络训练,最终得到基于神经网络信息融合的采煤机煤岩识别模型,通过实验验证研究的煤岩识别模型的性能,测试结果表明,实验研究的识别模型可以有效对煤岩界面进行识别,相比传统的单一传感器、单一识别技术,具有识别精度高、可靠性好、稳定性强等优点。
The coal petrography identification technology for the coal mining machine based on information fusion technolo- gy is studied. The multiple sensors instead of the traditional single sensor is used to establish the coal petrography identification system, and the fuzzy neural network algorithm is taken as the core algorithm to improve the stability, anti-interference ability and accuracy of the coal petrography identification for the coal mining machine. The vibration, resistance torque and motor cur- rent are monitored when the roller of the coal mining machine is cutting the coal wall, and the characteristic value is extracted by data collection. The characteristic value is conducted network training by fuzzy neural network algorithm, and the coal petrogra- phy identification model for coal mining machine based on neural network and information fusion is obtained. The performance of the coal petrography identification model is verified by experiments, and the results show this identification model can identify the coal petrography interface effectively, has higher identification precision, better reliability and stronger stability than that of the traditional single sensor and single identification technology.
出处
《现代电子技术》
北大核心
2015年第23期106-109,共4页
Modern Electronics Technique
关键词
采煤机
煤岩识别
模糊神经网络
多传感器
信息融合
coal mining machine
coal petrography identification
ANFIS
multi-sensor
information fusion
