Study on straightness monitoring method of scraper conveyor in intelligent fully-mechanized mining face

被引：0

作者：

Zhang F. ^{[1
,2
]}

Li C. ^{[1
]}

Li H. ^{[1
]}

机构：

[1] School of Electrical and Information Engineering, China University of Mining and Technology-Beijing, Beijing

[2] Institute of Intelligent Mining and Robotics, China University of Mining and Technology-Beijing, Beijing

来源：

Meitan Kexue Jishu/Coal Science and Technology (Peking) | 2022年 / 50卷 / 04期

关键词：

digital twin; intelligent fully-mechanized mining face; Kalman filter; scraper conveyor; straightness error; straightness monitoring;

D O I：

10.13199/j.cnki.cst.2020-0591

中图分类号：

学科分类号：

摘要：

The straightness error of the scraper conveyor in the fully-mechanized mining face is affected by the track detection error of the scraper conveyor and the movement error of the hydraulic support, which brings new challenges to the condition monitoring of the scraper conveyor. The real-time monitoring and control of the straightness of the scraper conveyor in fully-mechanized coal mining face and the acquisition of accurate and reliable position status information are essential for the intelligent mining of coal mines. In order to realize the automation, intelligence, unmanned straightening of the scraper conveyor in fully-mechanized mining face and effectively monitor the status of the scraper conveyor, this paper proposes a position state estimation method of the scraper conveyor based on Kalman filtering. Combined with the coal mining technology of the fully-mechanized coal mining face, a model of the straightening method of the scraper conveyor was established by using the detection track of the scraper conveyor. In view of the problem that the traditional method cannot reflect the movement state of the scraper conveyor in real time, the digital twin technology was used as the physical world and digital technology. The bridge of the world accurately reflects the position status information of the scraper conveyor in real time, studies the working characteristics of the “three machines” in the fully-mechanized mining face, and uses Kalman filter algorithm to effectively monitor the straightness of the scraper conveyor in the fully-mechanized mining face, and test the accuracy of the method by changing the normal distribution of detection error and shift error. The experimental results show that the monitoring method proposed in this paper can effectively reduce the influence of the detection error and the displacement error on the straightness of the scraper conveyor of the fully-mechanized mining face, and can still play an excellent effect when the detection error and the displacement error are large. The accuracy of monitoring can be increased by more than 30%. The method in this paper can make the straightness error of the scraper conveyor in the fully-mechanized mining face stable within a certain range, and improve the monitoring accuracy of the straightness of the scraper conveyor. © 2022 Meitan Kexue Jishu/Coal Science and Technology (Peking). All rights reserved.

引用

页码：246 / 255

页数：9

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