研究损坏和故障阶段的识别后填充驱动的声学排放数据的研究
Shuang Li1, Xiaoyong Dong2, Ran Ma1
1Zhonglian Runshi Xinjiang Coal Industry Co., Ltd., Changji, Xinjiang, China.
Scientific reports
|December 24, 2025
概括
本研究使用声辐射 (AE) 监测和机器学习来分类回填损坏阶段. 智能方法实现了93.52%的准确性,为采矿作业提供了可靠的早期预警.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 填充材料在采矿中对于稳定性和安全性至关重要.
- 了解和监测回填故障机制对于防止灾难性事件至关重要.
- 目前的监测方法可能缺乏用于早期损害检测的精度.
研究的目的:
- 开发一种智能系统,用于对填充材料的损伤和变形阶段进行分类.
- 使用声辐射 (AE) 监测数据进行实时故障分析.
- 提高地下采矿填埋场监测的可靠性.
主要方法:
- 对填充后材料进行了单轴压缩试验.
- 在故障期间捕获和分析声辐射 (AE) 参数.
- 进行了灵敏度分析,以选择关键的AE指数 (上升时间,计数,能量,持续时间,振幅).
- 使用实验AE数据开发并验证了一种反向传播 (BP) 神经网络模型.
主要成果:
- 确定了敏感于回填损坏进展的关键AE参数.
- 对于损伤和变形阶段,BP神经网络模型实现了93.52%的分类准确度.
- 证明了该模型在分类不同阶段的填充故障中的有效性.
结论:
- 建议使用AE监控和机器学习的智能方法对分类回填损害有效.
- 这种方法提供了一个可靠的工具,用于实时监控和提前警告在采矿的回填故障.
- 这项研究增强了对回填物材料故障机制的理解.
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