相关实验视频
Updated: Jul 28, 2025

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Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
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在重复采矿的情况下,使用早期预警系统进行故障分析和预测屋顶不稳定性
Fei Li1, Dezhong Kong2,3, Qiang Li4
1Mining College, Guizhou University, Guiyang, 550025, China.
Scientific reports
|May 30, 2023
概括
在近距离的煤层中,使用颗粒弧形模型和辐射基函数神经网络 (RBFNN) 预测了屋顶不稳定性. 该系统有助于防止在重复采矿过程中发生屋顶洞穴事故.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 地质技术工程 地质技术工程
- 计算智能是一种计算智能.
背景情况:
- 在近距离的煤层中重复开采会造成屋顶洞穴事故的风险.
- 了解覆盖层的机械行为对于矿山安全至关重要.
研究的目的:
- 建立一个机械模型来预测屋顶的不稳定性.
- 开发一个针对屋顶事故的智能预警系统.
主要方法:
- 颗粒状形结构的机械建模.
- 辐射基函数神经网络 (RBFNN) 的应用用于预测.
- 开发一个全面的早期预警系统.
主要成果:
- 确定了影响屋顶洞穴的关键因素:采矿高度,前进速度,距离,尖端距离,岩石强度和支负载.
- RBFNN模型准确地预测了屋顶沉降,煤墙变形和支负载.
- 现场应用证明了该系统的实际价值和有效性.
结论:
- 开发的预测模型和早期预警系统对于智能预测和预防煤矿停止灾害具有重要意义.
- 该系统通过减轻屋顶灾难风险,提高了安全高效的煤矿生产.
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