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驾驶面上的气体爆发警告方法:通过Optuna优化,自适应规范化和变压器框架来改进方法.
Zhenguo Yan1, Zhixin Qin1, Jingdao Fan1
1College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究介绍了矿山气体预警系统的混合模型,提高了准确性和适应性. 它增强了预测能力,在智能煤炭开采操作中为气体爆发提供了早期警报.
科学领域:
- 矿山安全工程 矿山安全工程
- 数据科学是数据科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 气体预警系统面临诸如指标有限,适应性差,建模不准确等挑战.
- 在各种矿山中广泛应用,数据影响警告准确性不足.
研究的目的:
- 开发一种混合预测和预警模型,用于在矿山中检测气体.
- 解决当前气体预警系统的局限性,提高准确性和适应性.
主要方法:
- 适应性规范化 (AN) 用于时间序列数据标准化,加上门式循环单位 (GRU).
- 组合实证模式分解 (EEMD) 用于特征提取和变化模式分解 (VMD) 顺序选择.
- 增强的变压器框架用于非线性和长时间序列分析,使用Optuna和xgbRegressor进行优化.
主要成果:
- 混合动力车型实现了0.980975的R平方和0.000149的MAE,超过了RNN,GRU,LSTM和BiLSTM.
- 引导被用来估计混合模型的置信区间,解决数据稀缺问题.
- 维度分析创建了实时气体排放指标;异常检测使无监督的气体爆发预警能够实现.
结论:
- 拟议的混合模型展示了矿山中气体危险的强有力的预测和预警能力.
- 这种方法为推进智能煤矿运营提供了技术增强.
- 该模型有效地处理数据稀缺性,并在不同的采矿场景中提高了多功能性.
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