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基于 MNRS 的综采工作面液压支架支护状态 评估参数约简方法.

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Title: 基于 MNRS 的综采工作面液压支架支护状态 评估参数约简方法.
Alternate Title: Parameter simplification methods for assessing support status of hydraulic supports in fully-mechanized working face based on MNRS.
Authors: 张传伟1,2 zhangcw@xust.edu.cn, 黄骏峰1, 路正雄1 19122781931@163.com, 李林岳1, 田嘉玮1, 马悦杨1, 赵昶轩1, 杨 彬1
Source: Coal Science & Technology (0253-2336). Jun2026, Vol. 54 Issue 6, p363-376. 14p.
Subject Terms: *Rough sets, *Feature selection, *Classification, *Data quality
Abstract (English): Aiming at the problem that the accurate evaluation of hydraulic roof support status can not be achieved due to heavy noise, multiple parameters, data redundancy, and uneven sample distribution in monitoring data for hydraulic roof support in fully-mechanized working faces, a parameter reduction method based on an improved neighborhood rough set is proposed to enhance the accuracy of support status evaluation. First, a decision information system for parameter reduction of hydraulic roof support status is constructed by analyzing the characterization parameters and decision parameters of support status. Then, to address the issues of uneven sample distribution and noise interference, a sample similarity index is introduced, a neighborhood partitioning mechanism integrated with sample quality evaluation is established, and a parameter reduction model named MNRS based on parameter dependency is proposed. Finally, support status data from 150 sets of hydraulic roof supports at Hecaogou No. 2 Coal Mine in Yan’an City are collected, and the performance of the proposed model is validated by comparing it with the RS model, the NRS model, and the KNNRS parameter reduction model. The results show that the proposed MNRS model reduces 9 key parameters, achieving a parameter reduction ratio of 35.71%, which represents improvements of 21.42%, 21.42%, and 14.28% over the traditional rough set (RS), neighborhood rough set (NRS), and k-nearest neighbor rough set (KNNRS) models, respectively. Furthermore, based on the reduced parameters, the KNN classification accuracy reaches 97.14% (compared to 92.88% on the original data), the mean squared error (SME) is reduced to 0.028 6 (a decrease of 49.74%), and the symmetric mean absolute percentage error (ESMAP) is only 1.14%. For sample sizes ranging from 50 to 150, the MNRS model achieves an average classification accuracy of 96.77%, with the average MSE and average SMAPE of only 0.036 7 and 1.38%, respectively, demonstrating significantly superior robustness compared to the benchmark models. Therefore, the proposed method effectively addresses the sensitivity to noise, uneven sample distribution, and incomplete redundancy elimination of traditional models, enabling accurate selection of key parameters for hydraulic roof support status evaluation. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 针对综采工作面液压支架支护状态监测数据噪声严重、参数多、数据冗余且样本分布不均匀, 导致支架支护状态无法准确评价的问题, 提出一种基于改进邻域粗糙集的液压支架支护状态参数约 简方法, 以提升支护状态评价的准确性。该方法通过分析综采工作面液压支架支护状态表征参数及 决策参数, 构建了液压支架支护状态参数约简决策信息系统; 针对数据分布不均与噪声干扰问题, 引入样本相似指数 SIM k (x), 构建融合样本质量评价的邻域划分机制, 提出了基于参数依赖度的液压 支架支护状态参数约简模型 MNRS。最后, 采集延安市禾草沟二号煤矿 150 台液压支架的支护状态 数据, 并与 RS 模型、NRS 模型及 KNNRS 参数约简模型进行对比, 验证所提模型的性能。结果表明: 研究提出的 MNRS 模型约简出 9 项关键参数, 参数约简率达 35.71%, 较传统粗糙集 (RS) 、邻域粗 糙集 (NRS) 和 k 近邻粗糙集 (KNNRS) 分别提升 21.42% 、21.42% 和 14.28% ;基于约简参数的 KNN 分类准确率达 97.14% (原始数据为 92.88%), 均方误差 S ME降至 0.028 6 (降幅 49.74%), 对称平均绝 对百分比误差 E SMAP仅为 1.14% ;在 50～150 组样本规模下, MNRS 模型平均分类准确率达到 96.77%, 均方误差均值及对称平均绝对百分比误差均值仅为 0.036 7 和 1.38%, 鲁棒性显著优于对比 模型。因此, 所提方法能有效解决传统模型对噪声敏感、样本分布不均、冗余参数剔除不彻底的问 题, 实现液压支架支护状态评估关键参数的准确筛选。 [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Data: 基于 MNRS 的综采工作面液压支架支护状态 评估参数约简方法.
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  Data: Parameter simplification methods for assessing support status of hydraulic supports in fully-mechanized working face based on MNRS.
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  Data: <searchLink fieldCode="AR" term="%22张传伟%22">张传伟</searchLink><relatesTo>1,2</relatesTo><i> zhangcw@xust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22黄骏峰%22">黄骏峰</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22路正雄%22">路正雄</searchLink><relatesTo>1</relatesTo><i> 19122781931@163.com</i><br /><searchLink fieldCode="AR" term="%22李林岳%22">李林岳</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22田嘉玮%22">田嘉玮</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22马悦杨%22">马悦杨</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22赵昶轩%22">赵昶轩</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22杨+彬%22">杨 彬</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Coal+Science+%26+Technology+%280253-2336%29%22">Coal Science & Technology (0253-2336)</searchLink>. Jun2026, Vol. 54 Issue 6, p363-376. 14p.
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  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Rough+sets%22">Rough sets</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br />*<searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: Aiming at the problem that the accurate evaluation of hydraulic roof support status can not be achieved due to heavy noise, multiple parameters, data redundancy, and uneven sample distribution in monitoring data for hydraulic roof support in fully-mechanized working faces, a parameter reduction method based on an improved neighborhood rough set is proposed to enhance the accuracy of support status evaluation. First, a decision information system for parameter reduction of hydraulic roof support status is constructed by analyzing the characterization parameters and decision parameters of support status. Then, to address the issues of uneven sample distribution and noise interference, a sample similarity index is introduced, a neighborhood partitioning mechanism integrated with sample quality evaluation is established, and a parameter reduction model named MNRS based on parameter dependency is proposed. Finally, support status data from 150 sets of hydraulic roof supports at Hecaogou No. 2 Coal Mine in Yan’an City are collected, and the performance of the proposed model is validated by comparing it with the RS model, the NRS model, and the KNNRS parameter reduction model. The results show that the proposed MNRS model reduces 9 key parameters, achieving a parameter reduction ratio of 35.71%, which represents improvements of 21.42%, 21.42%, and 14.28% over the traditional rough set (RS), neighborhood rough set (NRS), and k-nearest neighbor rough set (KNNRS) models, respectively. Furthermore, based on the reduced parameters, the KNN classification accuracy reaches 97.14% (compared to 92.88% on the original data), the mean squared error (SME) is reduced to 0.028 6 (a decrease of 49.74%), and the symmetric mean absolute percentage error (ESMAP) is only 1.14%. For sample sizes ranging from 50 to 150, the MNRS model achieves an average classification accuracy of 96.77%, with the average MSE and average SMAPE of only 0.036 7 and 1.38%, respectively, demonstrating significantly superior robustness compared to the benchmark models. Therefore, the proposed method effectively addresses the sensitivity to noise, uneven sample distribution, and incomplete redundancy elimination of traditional models, enabling accurate selection of key parameters for hydraulic roof support status evaluation. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Chinese)
  Group: Ab
  Data: 针对综采工作面液压支架支护状态监测数据噪声严重、参数多、数据冗余且样本分布不均匀, 导致支架支护状态无法准确评价的问题, 提出一种基于改进邻域粗糙集的液压支架支护状态参数约 简方法, 以提升支护状态评价的准确性。该方法通过分析综采工作面液压支架支护状态表征参数及 决策参数, 构建了液压支架支护状态参数约简决策信息系统; 针对数据分布不均与噪声干扰问题, 引入样本相似指数 SIM k (x), 构建融合样本质量评价的邻域划分机制, 提出了基于参数依赖度的液压 支架支护状态参数约简模型 MNRS。最后, 采集延安市禾草沟二号煤矿 150 台液压支架的支护状态 数据, 并与 RS 模型、NRS 模型及 KNNRS 参数约简模型进行对比, 验证所提模型的性能。结果表明: 研究提出的 MNRS 模型约简出 9 项关键参数, 参数约简率达 35.71%, 较传统粗糙集 (RS) 、邻域粗 糙集 (NRS) 和 k 近邻粗糙集 (KNNRS) 分别提升 21.42% 、21.42% 和 14.28% ;基于约简参数的 KNN 分类准确率达 97.14% (原始数据为 92.88%), 均方误差 S ME降至 0.028 6 (降幅 49.74%), 对称平均绝 对百分比误差 E SMAP仅为 1.14% ;在 50～150 组样本规模下, MNRS 模型平均分类准确率达到 96.77%, 均方误差均值及对称平均绝对百分比误差均值仅为 0.036 7 和 1.38%, 鲁棒性显著优于对比 模型。因此, 所提方法能有效解决传统模型对噪声敏感、样本分布不均、冗余参数剔除不彻底的问 题, 实现液压支架支护状态评估关键参数的准确筛选。 [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194880206
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      – SubjectFull: Rough sets
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      – SubjectFull: Feature selection
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      – SubjectFull: Classification
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            – D: 01
              M: 06
              Text: Jun2026
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              Y: 2026
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