Design of intelligent monitoring system for mining machines based on CNN and SVDD

GE Yao; SUN Qiang; WANG Kun-dong

doi:10.13873/J.1000-9787(2016)10-0076-04

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Design of intelligent monitoring system for mining machines based on CNN and SVDD

更新时间：2025-09-11

- Design of intelligent monitoring system for mining machines based on CNN and SVDD
- Vol. 35, Issue 10, Pages: 76-79(2016)
- 作者机构：
  
  1. 上海交通大学精密工程及智能系统研究所
  2. 兖矿东华重工有限公司采掘装备制造分公司
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)10-0076-04
  CLC： TD632.2;TP274
- Published：2016
- 稿件说明：
移动端阅览
[1]葛垚,孙强,王坤东.基于CNN与SVDD的掘进机智能监测系统设计[J].传感器与微系统,2016,35(10):76-79.

GE Yao, SUN Qiang, WANG Kun-dong. Design of intelligent monitoring system for mining machines based on CNN and SVDD[J]. 2016, 35(10): 76-79.
[1]葛垚,孙强,王坤东.基于CNN与SVDD的掘进机智能监测系统设计[J].传感器与微系统,2016,35(10):76-79. DOI： 10.13873/J.1000-9787(2016)10-0076-04.

GE Yao, SUN Qiang, WANG Kun-dong. Design of intelligent monitoring system for mining machines based on CNN and SVDD[J]. 2016, 35(10): 76-79. DOI： 10.13873/J.1000-9787(2016)10-0076-04.

摘要

通过激光标志物的卷积神经网络（CNN）检测

与标志物中心点的奇异值分解（SVD）重构

实现了掘进机在巷道坐标系下的坐标估计。通过基于支撑向量数据描述（SVDD）的陀螺仪静止状态抖动抑制

与参考系变换

实现了机身与掘进臂的姿态检测。通过基于Open GL的图形学引擎

实现了工作面场景的实时虚拟渲染。测试结果表明:系统能够准确可靠地完成工作面场景下掘进机监测任务。

Abstract

By detection of convolutional neural network（ CNN） of laser marker and reconstruction of singular value decomposition（ SVD） of center of markers

coordinates estimation of machine in tunnel coordinate is achieved. By jitter suppression of gyro in resting state based on supporting vector data description（ SVDD） and transformation of reference system

body and robotic arm altitude estimation are achieved. Based on Open GL

real-time virtual rendering of scene of working face is achieved. Experimental results show that the system is able to monitor mining machine in working face scene accurately and reliably.

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references

基于Kalman滤波算法的姿态传感器信号融合技术研究 [J]. 陈航科,张东升,盛晓超,王凯. 传感器与微系统 . 2013(12)

双目视觉在移动机器人定位中的应用 [J]. 王殿君. 中国机械工程 . 2013(09)

模板匹配技术在图像识别中的应用 [J]. 田娟,郑郁正. 传感器与微系统 . 2008(01)

基于MEMS惯性传感器的机器人姿态检测系统的研究 [J]. 秦勇,臧希喆,王晓宇,赵杰,蔡鹤皋. 传感技术学报 . 2007(02)

Support Vector Data Description [J] . David M.J. Tax,Robert P.W. Duin. Machine Learning . 2004 (1)

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