液压支架跟机移架控制异常工况模式识别方法

马志涛; 付翔; 李浩杰; 牛鹏昊; 贾一帆

doi:10.13272/j.issn.1671-251x.2025030036

液压支架跟机移架控制异常工况模式识别方法

马志涛^1,,
付翔^{1, 2, 3, ,},
李浩杰¹,
牛鹏昊¹,
贾一帆¹

1.
太原理工大学矿业工程学院，山西太原　030024
2.
智能采矿装备技术全国重点实验室，山西太原　030024
3.
山西焦煤集团有限责任公司博士后工作站，山西太原　030024

基金项目:

国家自然科学基金项目（52274157）；山西省基础研究计划联合资助项目（202403011241002）；“科技兴蒙”行动重点专项项目（2022EEDSKJXM010）。

详细信息

作者简介:
马志涛（2001—），男，山西大同人，硕士研究生，研究方向为智能采矿数智赋能技术，E-mail：2236053015@qq.com

通讯作者:
付翔（1986—），男，山西长治人，副教授，博士（后），博士研究生导师，研究方向为智能采矿数智赋能技术，E-mail:14632235@qq.com。

中图分类号: TD355
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出版历程
- 收稿日期: 2025-03-07
- 修回日期: 2025-04-09
- 网络出版日期: 2025-05-06
- 刊出日期: 2025-04-14

Method for recognizing abnormal operation patterns in hydraulic support machine-following and shifting control

MA Zhitao^1,,
FU Xiang^{1, 2, 3, ,},
LI Haojie¹,
NIU Penghao¹,
JIA Yifan¹

1.
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2.
State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030024, China
3.
Post-doctoral workstation, Shanxi Coking Coal Group Co., Ltd., Taiyuan 030024, China

摘要

摘要:
智采工作面在液压支架自动跟机时，由于底板、液压系统及电液控制系统等多方面因素的影响，会出现丢架、直线度不平整等异常工况。当前针对各类异常工况的识别分析主要集中于自动跟机结束后，仅通过人工调整进行单一判断，不利于快速判断需人工调控的液压支架架号。针对上述问题，提出了一种液压支架跟机移架控制异常工况模式识别方法，将异常工况识别范围提前至支架降柱后的移架阶段，得出停机波动型、移架超时型和行程异常型3类异常工况模式。首先，采集液压支架油缸行程与立柱压力数据。其次，对数据进行预处理，包括异常值处理、相邻数据求差及依据差值正负合并数据。然后，采用基于行程−压力−时间分析的移架异常识别算法对停机波动型与移架超时型模式进行识别；采用决策树模型对行程异常型模式进行识别。最后，提取移架动作起始及结束时间、当前支架行程的最大与最小值、当前支架与两侧支架的行程差6项特征，将其中移架动作起始与结束时间输入移架异常识别算法，进行行程波动识别，对具有行程波动的数据分别进行压力波动及移架动作时间的判别，识别出停机波动型与移架超时型模式；将后续4项行程类特征输入决策树模型，进行行程异常类模式的识别。实际测试结果表明：该识别方法对停机波动型模式与移架超时型模式的识别精确率为100%，召回率达95%以上；对于行程异常突降型模式的识别精确率为100%，召回率为97.87%；行程异常均小型模式的识别精确率为95.29%，召回率为81%，能够较好地对液压支架跟机移架控制的异常工况进行识别。
- 液压支架 /
- 自动跟机 /
- 推溜移架 /
- 异常工况识别 /
- 油缸行程 /
- 立柱压力
Abstract:
In intelligent mining faces, abnormal operating conditions such as support loss and poor linearity may occur during automatic follow-up of hydraulic supports, due to various factors including the floor conditions, hydraulic system, and electro-hydraulic control system. Currently, the identification and analysis of these abnormal conditions mainly occur after the automatic follow-up process, relying solely on manual adjustments for judgment. This approach is inefficient for quickly determining which hydraulic support units require manual intervention. To address this issue, a method was proposed for identifying abnormal condition patterns in machine-following and shifting control of hydraulic supports. This method shifted the scope of anomaly detection to the shifting stage after the support has lowered its columns, and classifies abnormal patterns into three types: shutdown fluctuation type, over-time shifting type, and stroke anomaly type. Data on hydraulic support cylinder stroke and leg pressure were collected. Then, data preprocessing was performed, including outlier removal, calculation of differences between adjacent data points, and merging data based on the sign of the differences. An anomaly recognition algorithm based on stroke-pressure-time analysis was used to identify the shutdown fluctuation and over-time shifting types. A decision tree model was employed to detect stroke anomaly patterns. Six key features were extracted: start and end time of the shifting action, maximum and minimum values of the current support's stroke, and the stroke differences between the current support and its neighboring supports. The start and end times were fed into the stroke anomaly recognition algorithm to detect fluctuation patterns. For data with stroke fluctuations, further analysis of pressure variation and shifting action duration was conducted to identify shutdown fluctuation and over-time shifting types. The remaining four stroke-related features were input into the decision tree model to identify stroke anomaly patterns. Experimental results show that the proposed method achieved a precision of 100% and recall rate of over 95% for identifying shutdown fluctuation and over-time shifting types. For the sudden drop type stroke anomaly, the method achieved a precision of 100% and a recall rate of 97.87%. For the uniform small stroke anomaly, the precision was 95.29% and the recall rate was 81%. These results demonstrate that the method effectively identifies abnormal conditions in hydraulic support machine-following and shifting control.
- hydraulic support /
- automatic machine-following /
- rack shifting /
- abnormal condition recognition /
- cylinder stroke /
- leg pressure

HTML全文

图 1 正常工况下的油缸行程及立柱压力

Figure 1. Cylinder stroke and column pressure under normal working conditions

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图 2 液压支架跟机移架过程中的3种异常工况

Figure 2. Three abnormal working conditions during the hydraulic support machine-following and shifting

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图 3 行程异常型2类子模式

Figure 3. Two sub-patterns of abnormal stroke type

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图 4 液压支架跟机移架异常工况模式识别流程

Figure 4. Process of abnormal operating condition pattern recognition for hydraulic support machine-following and shifting

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图 5 决策树模型的混淆矩阵

Figure 5. Confusion matrix of decision tree model

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表 1 异常工况模式分类

Table 1 Classification of abnormal operating condition patterns

名称	数据表现	原因分析
停机波动型	液压支架正常跟机移架过程中出现立柱行程波动与立柱升降	丢架；液压系统短时压力故障；电液控制系统动作控制不合理等
移架超时型	液压支架跟机移架过程中出现较长时间停机现象	丢架；液压系统故障；电液控制系统故障等
行程异常型	液压支架油缸行程过小	丢架；底板不平；工作面调直导致支架运动受限等

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表 2 行程异常型子模式分类

Table 2 Sub-pattern classification of abuormal stroke type

名称	数据表现	原因分析
行程异常突降型	中间支架与其相邻支架间油缸行程值存在明显差距	丢架；底板不平等
行程异常均小型	中间支架与其相邻支架间油缸行程值无明显差距	工作面调直导致支架运动受限；底板不平等

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表 3 模式识别实例化测试

Table 3 Pattern recognition implementation test

	数量	比例%
样本总量	10 218	100
停机波动型	94	0.86
移架超时型	88	0.92

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表 4 各模型交叉验证及测试集准确率

Table 4 Cross-validation and test set accuracy of each model　%

模型	交叉验证平均准确率	测试集准确率
决策树	94.8	94.2
K最近邻	93.3	92.5
支持向量机	92.2	91.6

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表 5 模型验证结果

Table 5 Model validation results %

模式类型	精确率	召回率
正常	96.32	99.60
突降型	100	97.87
均小型	95.29	81.00

下载: 导出CSV

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