4
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Attitude monitoring method for hydraulic support in fully mechanized working face based on PSO-ELM

      research-article

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In response to the problems of cumulative errors and inaccurate correction results in the attitude calculation method of hydraulic supports based on inertial measurement units, a fully mechanized working face hydraulic support attitude monitoring method based on particle swarm optimization (PSO) − extreme learning machine (ELM) is proposed. Using the pitch angle of the hydraulic support top beam as the monitoring object, a tilt sensor and gyroscope are used to collect real-time information on the support attitude of the hydraulic support top beam. The collected data is preprocessed and input into the PSO-ELM error compensation model to obtain the predicted solution error. At the same time, the hydraulic support attitude is calculated through Kalman filtering fusion to obtain the calculated value. Then the method uses the error prediction value to compensate for the error in the calculated value, in order to obtain more accurate data on the top beam support attitude. This method only considers the relationship between acceleration and angular velocity data and solution errors, without relying on specific physical models. It can effectively reduce the cumulative error of attitude solution. The experimental results show that the average absolute error of the pitch angle of the top beam of the hydraulic support has been reduced from 1.420 8° before compensation to 0.058 0°. The error curve has good convergence, verifying that the proposed method can sustainably and stably monitor the support attitude of the hydraulic support.

          Abstract

          摘要:针对基于惯性测量单元的液压支架姿态解算方法会产生累计误差、校正结果不准确的问题, 提出一种 基于粒子群优化(PSO)-极限学习机(ELM)的综采工作面液压支架姿态监测方法。以液压支架顶梁俯仰角为监 测对象, 采用倾角传感器和陀螺仪采集液压支架顶梁支护姿态实时信息, 对采集到的数据进行预处理, 将处理后 的数据输入PSO-ELM误差补偿模型中, 得到解算误差预测值; 同时通过卡尔曼滤波融合进行液压支架姿态解 算, 得到解算值;再用误差预测值对解算值进行误差补偿, 从而求得更加准确的顶梁支护姿态数据。该方法只考 虑加速度和角速度数据与解算误差的关系, 不依赖具体的物理模型, 可有效降低姿态解算累计误差。实验结果表 明:液压支架顶梁俯仰角平均绝对误差由补偿前的1.420 8°减少到0.058 0°, 且误差曲线具有良好的收敛性, 验证 了所提方法可持续稳定地监测液压支架的支护姿态。

          Related collections

          Author and article information

          Journal
          JMA
          Journal of Mine Automation
          Editorial Department of Industry and Mine Automation (China )
          1671-251X
          01 August 2024
          24 December 2024
          : 50
          : 8
          : 14-19
          Affiliations
          [1] 1National and Provincial Joint Engineering Laboratory of Mining Intelligent Electrical Apparatus Technology, Taiyuan University of Technology, Taiyuan 030024, China
          [2] 2Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control, Taiyuan University of Technology, Taiyuan 030024, China
          Article
          j.issn.1671-251x.2024070023
          10.13272/j.issn.1671-251x.2024070023
          9729f1ed-2772-4121-8abb-528cb0b90b73
          © 2024 Editorial Department of Industry and Mine Automation

          This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

          History
          Categories
          Journal Article

          particle swarm optimization,attitude monitoring,top beam pitch angle,hydraulic support,PSO-ELM,extreme learning machine,error compensation

          Comments

          Comment on this article

          scite_
          0
          0
          0
          0
          Smart Citations
          0
          0
          0
          0
          Citing PublicationsSupportingMentioningContrasting
          View Citations

          See how this article has been cited at scite.ai

          scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

          Similar content431