期刊文献+

煤矿带式输送机矸石识别新方法

Novel Method for Identifying Gangue in Coal Mine Belt Conveyor
下载PDF
导出
摘要 带式输送机煤矸石高效分拣是实现煤炭资源绿色开采的重要途径,其核心问题是煤和矸石的快速精准识别。因此提出了一种基于电磁波探测的带式输送机煤矸石识别方法。首先,结合电磁波信号在煤矸石分界表面的反射和透射现象,分析了探测过程中电磁信号的传播特性;然后,利用连续小波变换(CWT)将电磁信号映射为二维时频图,并将其作为输入对MobileNetV2煤矸石识别模型进行训练和验证,输出识别结果。实验结果表明,MobileNetV2模型的平均准确率和平均F1分别可达97.58%和92.75%,验证了所提方法对带式输送机煤矸石识别的可行性和有效性。 Efficient sorting of coal and gangue in belt conveyor is an important way to realize green mining of coal resources,and its core problem is the rapid and accurate identification of coal and gangue.Therefore,proposed a recognition method of coal and gangue in belt conveyor based on electromagnetic wave detection.Firstly,the propagation characteristics of electromagnetic signals in the detection process were analyzed by combining the reflection and transmission phenomena of electromagnetic wave signals on the surface of coal and gangue demarcation.Then,the electromagnetic signals were mapped into two-dimensional time-frequency diagrams by using continuous wavelet transform(CWT),which is used as input to train and validate the MobileNetV2 coal and gangue recognition model and output the recognition results.The experimental results show that the average accuracy and average F1 of the MobileNetV2 model can reach 97.58%and 92.75%respectively,which verifies the feasibility and validity of the proposed method for the recognition of coal and gangue in belt conveyor.
作者 张海波 李晓真 刘扬 Zhang Haibo;Li Xiaozhen;Liu Yang(Licun Coal Mine,Lu’an Chemical Group,Changzhi 046600,China;School of Mechanical and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China)
出处 《煤矿机械》 2025年第1期154-157,共4页 Coal Mine Machinery
基金 江苏省自然科学基金面上项目(BK20211245)。
关键词 电磁波探测 深度学习 煤矸识别 时频映射 MobileNetV2 electromagnetic wave detection deep learning coal and gangue recognition time-frequency mapping MobileNetV2
  • 引文网络
  • 相关文献
;
使用帮助 返回顶部