基于深度学习的异常检测框架用于采矿安全中的液压支持系统
Wei Xin1, Longhe Liu2, Jiyu Wang3
1School of Mines, China University of Mining and Technology, Xuzhou, 221116, China. cumtxw@cumt.edu.cn.
Scientific reports
|October 8, 2025
概括
这项研究引入了一个深度学习框架,用于检测煤矿液压支压力数据中的异常. 该模型有效地识别出不寻常的压力模式,增强安全监控.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 煤矿安全在很大程度上依赖于监控液压支系统.
- 这些系统中的异常压力变化可能表明潜在的故障.
- 现有的监测方法可能缺乏检测微妙异常的复杂性.
研究的目的:
- 开发和评估一种新的深度学习框架,用于检测煤矿液压支压力数据中的异常.
- 为了提高识别异常压力模式的准确性和效率.
- 提高煤矿运营的安全性和可靠性.
主要方法:
- 提出了一个结合双向LSTM和CNN架构的深度学习框架.
- 封闭的剩余连接和自我注意机制被纳入,以捕捉时间和局部特征.
- 数据预处理包括时间重新抽样,缺失值赋值和规范化.
- 该模型经过训练,并根据中国西部矿山10个液压支器的压力数据进行了测试.
主要成果:
- 拟议的框架在异常检测任务中表现出色.
- 废弃性研究证实了CNN层 (472%的测试损失增加) 和封闭的残留机制 (352%的测试损失增加) 的显著贡献.
- 该模型有效地重建了样本并确定了异常,区分正常和异常的压力变化.
结论:
- 新的深度学习框架为水力支压力数据中异常检测提供了一个有希望的方法.
- 像CNN层和封闭的残余机制这样的关键组件对于模型性能至关重要.
- 局限性包括数据质量依赖性,固定值策略和计算复杂性,需要进一步研究.
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