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《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:

期数:

2025年02期

页码:

122-128

栏目:

出版日期:

2025-04-15

文章信息/Info

Title:

Automatic calibration method of wind speed instrument of mine fan based on big data mining

文章编号:

1000-0682（2025）02-0122-07

作者:

廖家新

（1.安徽煤矿安全监察局安全技术中心；2.安徽矿安检测技术服务有限公司，安徽合肥 230088）

Author(s):

LIAO Jiaxin

(1.Safety Technology Center of Anhui Coal Mine Safety Supervision Bureau, Anhui Hefei 230088, China; 2.Anhui Mine Safety Testing Technology Service Co., Ltd, Anhui Hefei 230088, China)

关键词:

大数据挖掘; 矿山; 通风机; 风速仪表; 自动校准

Keywords:

big data mining;  mines;  ventilator;  anemometer;  automatic calibration

分类号:

TD723

DOI:

10.19950/j.cnki.CN61-1121/TH.2025.02.022

文献标志码:

A

摘要:

在矿山通风机DCS系统的风速仪表校准中，受到出风口数据冗余干扰的影响，存在校准精度不高的问题。针对这一情况，提出基于大数据挖掘的矿山通风机风速仪表自动校准方法。在研究过程中，根据矿山通风DCS系统的特点和需求，建立巷道数学模型和物理模型，确定传感器位置，实时准确收集通风机风速仪表的运行数据。在此基础上，利用大数据挖掘技术建立关键数据集成架构，对收集到的数据进行查询和集成，获得自动校准的关键数据，排除冗余数据的影响，确定最佳的校准参数完成风速仪表的自动校准。实验结果表明：面对不同的工况，提出的基于大数据挖掘的自动校准方法取得了显著的效果，与其他校准方法相比，该方法具有更高的校准精度和更低的误差率，能够适应不同矿山通风系统的需求。

Abstract:

In the calibration of wind speed instruments in the DCS system of mine ventilation fans, there is a problem of low calibration accuracy due to the interference of redundant air outlet data. In response to this situation, a big data mining based automatic calibration method for mine ventilation fan wind speed instruments is proposed. During the research process, based on the characteristics and requirements of the mine ventilation DCS system, a mathematical and physical model of the tunnel was established, the sensor position was determined, and real-time and accurate operational data of the ventilation fan wind speed instrument was collected. On this basis, big data mining technology is used to establish a key data integration architecture, query and integrate the collected data, obtain key data for automatic calibration, eliminate the influence of redundant data, and determine the optimal calibration parameters to complete the automatic calibration of wind speed instruments. The experimental results show that the proposed automatic calibration method based on big data mining has achieved significant results in different working conditions. Compared with other calibration methods, this method has higher calibration accuracy and lower error rate, and can adapt to the needs of different mine ventilation systems.

参考文献/References:

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备注/Memo

备注/Memo:

收稿日期：2024-07-19第一作者：廖家新（1984—），男（汉族），安徽金寨人，硕士，高级工程师，研究方向为矿山机电设备的检测检验技术及标准研究。E-mail：jxliao6@163.com

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