一种预测煤矿甲喷发量和检测异常的方法,基于二次分解和ETO-TSMixer的融合模型
Qiangyu Zheng1, Cunmiao Li1, Bo Yang2
1College of Arts, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究引入了一种改进的TSMixer模型,用于预测煤矿中甲爆发量,提高安全性. 这种新的方法为气体灾害提供了更准确,更可靠的早期预警.
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
- 计算科学 计算科学
背景情况:
- 煤矿中的甲喷发带来了重大安全风险.
- 现有的预测模型因复杂性和普遍性差而受到影响.
- 准确预测甲爆发量对于预防事故至关重要.
研究的目的:
- 开发一种新的甲爆发量预测和预警方法.
- 解决现有模型的局限性,包括复杂性和概括性.
- 通过改进的灾难预测技术,提高煤矿安全.
主要方法:
- 利用STL分解和自适应的萨维茨基-戈莱过来实现数据平滑和减少时间复杂性.
- 使用CEEMDAN-Kmeans-VMD二次分解与K-means集群集成内在模式函数 (IMFs).
- 使用指数三角优化 (ETO) 算法和精细的TSMixer模型与可逆实例规范化 (RevIn) 的优化变化模式分解 (VMD) 参数.
主要成果:
- 改进后的模型在测试组件上实现了优异的性能,RMSE为0.0151,MAE为0.0117,R2为0.9878
- 与参考模型相比,表现出显著的性能改进.
- 开发了一个异常检测框架,使用STL分解和双孤独森林进行强大的早期预警.
结论:
- 拟议的二次分解和改进的TSMixer模型显著提高了甲爆发量预测的准确性.
- 异常检测框架为煤矿天然气灾害预防提供可靠的动态预警支持.
- 这项研究为改善煤矿安全和灾害管理提供了有价值的工程应用见解.
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