超前感知综采工作面顶板来压并预判冒顶事故、自主评价支护参数的适应性,是提高综采工作面安全高效及智能化水平的基础。 

围绕综采工作面支架与顶板状态智能感知的核心问题,基于综采工作面电液控制液压支架海量监测数据,开发了综采工作面支架与顶板状态智能感知系统(SSRI);结合大数据挖掘及工作循环分析技术,提出了用于支架压力分析的多因次工作循环特征参数;研究了安全阀开启、割煤及邻架移架、地质等多种因素影响下的单台支架承载特征及支架群组载荷转移分布规律;在深入解读支架阻力及活柱下缩时序曲线所蕴含的支架与围岩相互作用关系的基础上,构建了支架与顶板状态智能感知模型,实现了对顶板来压的预测、冒顶预警、支架适应性及支护质量评价,初步建立了基于海量矿压监测数据的采场支架与顶板状态智能感知技术体系。

结果表明:① 额定工作阻力不应作为评价支架承载能力的唯一或主要指标,初撑力、额定工作阻力及安全阀开启特性等参数共同决定了支架的承载特性及承载能力;② 充分挖掘分析海量矿压监测数据,可以实现采场顶板灾害智能预警、支护质量评价及故障诊断等研究目标;③ 对海量监测数据的深入分析与利用是实现综采工作面支架围岩耦合自适应控制、支护参数自适应调整等智能化开采目标的前提与基础。

It is of great importance to sense the roof weighting and roof caving in advance and self-evaluate initial re- sistance and working resistance for promoting the safety,efficiency and intelligent mining level in longwall penal. Based on the scientific principles of intelligent sensing of supports-roof status,this paper used the massive data collected by the electro-hydraulic controlled two-leg shield supports,data mining and working cycle analysis technique to extract the characteristic parameters. Then the supporting characteristics of a single support and the load transformation and distri- bution on the longwall panel considering setting pressure, yielding, and shearer’ s cutting, as well as neighboring shields’ advance were investigated. On the basis of in-depth explanation on the temporal-sequence curve of supporting resistance and the indicating relationship between support and surrounding rocks,the intelligent sensing model was constructed. The study concludes that ① the rated working pressure should not be employed as the main indicator for evaluating the loading capacity of hydraulic support. Parameters,such as initial support pressure,rated working resist- ance and safety valve opening characteristics,would co-determine the bearing characteristics and capacity of hydraulic support. ② By analyzing the massive data of support pressure,the goals of intelligent roof disaster warning,support quality evaluation and fault diagnosis can be achieved. ③ The in-depth analysis and utilization of massive monitored pressure data is the foundation to realize intelligent mining goals such as the self-control of hydraulic support pressure and adaptive adjustment of support parameters.

1 数据处理与参数提取

　　1.1 数据的采集与处理

　　1.2 多因次工作循环特征参数提取

2 不同因素影响下支架与支架群组承载特征

　　2.1 初撑力对支架承载特征的影响

　　2.2 安全阀开启对支架承载特征的影响

　　2.3 割煤及邻架移架对支架及支架群组承载特征的影响

　　2.4 地质因素对支架承载特征的影响

3 矿压监测数据智能分析与应用

　　3.1 顶板来压分析与预测

　　3.2 工作面冒顶隐患预警

　　3.3 支护质量评价及故障诊断

4 结 论