基于气体监测数据和深度学习图像功能工程的地下异常传感器状态检测
Guoquan Chang1, Haoqian Chang2
1School of Computer Science, Anyang Institute of Technology, Henan, Anyang, 455000, China.
Heliyon
|November 30, 2023
概括
本研究引入了一种深度学习方法来监控地下气体传感器,使用传感器关系来检测故障. 这种方法通过确保可靠的气体度数据来提高采矿安全.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 传感器技术 传感器技术
- 数据科学数据科学数据科学
背景情况:
- 地下气体传感器对于采矿安全至关重要,但容易出现异常.
- 在复杂的采矿环境中,手动监控众多传感器是不可行的.
- 准确识别气体传感器状态对于防止事故至关重要.
研究的目的:
- 为分析地下气体传感器数据开发一种深度学习特征工程方法.
- 通过分析传感器之间的关系来建立检测传感器异常的方法.
- 提高地下矿山气体监测系统的可靠性.
主要方法:
- 利用深度学习功能工程来模拟地下气体传感器之间的关系.
- 将时间序列的气体度数据转换为复制图 (RP) 用于基于图像的分析.
- 分析了基于空气流模式的传感器 (T0,T1,T2) 之间的空间和时间相关性.
主要成果:
- 证明了传感器关系可以有效地利用位置和时间进行建模.
- 展示了故障传感器可以通过其他传感器的相关数据来检测出故障传感器.
- 确认了特定时间序列子节中的传感器之间的高相关性,这对于状态检查很有用.
结论:
- 拟议的深度学习方法准确地识别了地下气体传感器状态.
- 再现图和机器视觉方法提供了传感器数据的有效分析.
- 这种基于特征的分析使得矿业中可靠的气体度监测,预测和预警系统成为可能.
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