基于信息融合和神经网络的煤岩识别方法

雷静; 余斌

doi:10.13272/j.issn.1671-251x.2017.09.018

基于信息融合和神经网络的煤岩识别方法

雷静¹,
余斌^{2, 3}

1.
成都农业科技职业学院信息技术分院，四川成都611130
2.
中国矿业大学机电工程学院，江苏徐州221116
3.
新疆工程学院机械工程系，新疆乌鲁木齐830023

基金项目:

成都农业科技职业学院科研项目(CNY15-17)

详细信息

中图分类号: TD67
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- 刊出日期: 2017-09-09

Identification method of coal and rock based on information fusion and neural network

LEI Jing¹,
YU Bin^{2, 3}

1.
Information Technology Branch, Chengdu Agricultural College, Chengdu 611130, China
2.
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

摘要

摘要: 针对煤岩识别系统多采用单一传感器进行监测，存在识别精度、可靠度与稳定性均非常低的问题，提出一种基于信息融合和神经网络的煤岩识别方法。在现有采煤机上增加多种必要的传感器，采集采煤机不同工况下的电流、压力、振动频率、加速度等信号，采用小波包对采集的信号进行特征提取，并通过BP神经网络进行数据融合，从而实现对煤层和岩层的识别。真机实测结果表明，所提方法的识别误差在±0.5范围内，验证了其有效性。
- 煤炭开采 /
- 煤岩识别 /
- 采煤机 /
- 多传感器系统 /
- 信息融合 /
- 神经网络
Abstract: In view of problems that coal and rock identification systems use single sensor to monitor data and have low precision, reliability and stability, an identification method of coal and rock based on information fusion and neural network was proposed. A variety of necessary sensors are added to the existing shearer, which are used to collect current, pressure, vibration frequency, acceleration and other signals of the shearer under different situations. Wavelet packet is used for characteristics extraction, and BP neural network is used for data fusion, so as to achieve coal and rock identification. The test results of the real machine show that the identification error of the proposed method is within ±0.5, which verifies its validity.
- coal mining /
- coal and rock identification /
- shearer /
- multi-sensor system /
- information fusion /
- neural network

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2.	朱婉晋. 物联网采集缺失信息下的高精度虚拟重构算法. 信息技术与信息化. 2024(11): 44-48 . 百度学术

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文章访问数: 63
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基于信息融合和神经网络的煤岩识别方法

计量

出版历程

Identification method of coal and rock based on information fusion and neural network

期刊类型引用(2)

其他类型引用(2)

计量

出版历程

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