基于机器学习的煤矿通风机降噪工艺研究
Research on Noise Reduction Technology of Coal Mine Ventilation Fan Based on Machine Learning
张波1
作者信息
- 1. 六盘水化乐煤业有限公司,六盘水 553000
- 折叠
摘要
针对煤矿通风机噪声问题,提出一种基于机器学习的降噪工艺.通过采集与分析通风机振动和噪声信号,利用XGBoost算法构建噪声预测模型,并结合多目标优化算法生成最优降噪策略.实验结果表明,该工艺能够在不影响通风效果的前提下,显著降低通风机噪声,且降噪效果优于传统的被动降噪措施.研究成果拓展了机器学习在矿山机械降噪领域的应用,可为煤矿安全生产和职业健康提供新的解决方案.
Abstract
This article proposes a machine learning based noise reduction process for coal mine ventilation fans.By collecting and analyzing vibration and noise signals from ventilation fans,a noise prediction model is constructed using XGBoost algorithm,and the optimal noise reduction strategy is generated by combining multi-objective optimization algorithm.The experimental results show that this process can significantly reduce the noise of ventilation fans without affecting the ventilation effect,and the noise reduction effect is better than traditional passive noise reduction measures.The research results have expanded the application of machine learning in the field of mining machinery noise reduction,providing new solutions for coal mine safety production and occupational health.
关键词
煤矿通风机/机器学习/噪声预测/降噪工艺Key words
coal mine ventilation fan/machine learning/noise prediction/noise reduction process引用本文复制引用
出版年
2024
NETL
NSTL
万方数据