摘要
针对煤矿井下粉尘浓度数据的非线性、非平稳及强噪声特性导致传统预测模型精度不足的问题,提出一种变分模态分解(VMD)与长短期记忆网络(LSTM)融合的矿井粉尘浓度预测方法。将原始粉尘时序浓度数据输入VMD,在设定模态数量K和约束因子α条件下,VMD将原始数据分解为K个具有不同频率特征的模态分量,每个分量分别对应不同频段的振幅信息。将分量数据输入LSTM,通过选择性遗忘/输入门控算法对输入的分量数据进行训练,输出分量预测结果。对分量预测结果进行叠加重构,输出最终预测结果。以三道沟煤矿某工作面粉尘浓度数据为研究对象,分析了约束因子α对VMD分解效果的影响及模态数量K对预测性能的影响,结果表明:在K=5时样本被VMD完全分解,每个模态分量包含了详细的频率信息,可以清楚直观地分析整体信号的成分;α=2 000时各模态分量轮廓完整且完全分离,过小的α会导致独立分量中包含较多冗余信息,随着α值的增大模态分量带宽不断降低且分辨率提高。基于VMD-LSTM的粉尘浓度预测实验结果表明:在K=5,α=2 000时,VMD-LSTM的预测结果与实测值的误差最小,MAE,MSE,RMSE和MRE均优于其他模型,说明VMDLSTM对复杂环境条件下非线性、非平稳及强噪声的粉尘浓度预测具有强泛化能力和鲁棒性。
To address the problem of insufficient accuracy in traditional prediction models caused by the nonlinear,non-stationary,and strong noise characteristics of underground coal mine dust concentration data,a hybrid mine dust concentration prediction method integrating Variational Mode Decomposition(VMD)and a Long Short-Term Memory Network(LSTM)was proposed.The raw dust concentration time series data were fed into VMD.Under the set conditions for the number of modes K and the constraint factorα,VMD decomposed the raw data into K mode components with different frequency characteristics,with each component corresponding to amplitude information in different frequency bands.The component data were then fed into LSTM and trained using a selective forgetting/input gate algorithm to output the component prediction results.The component prediction results were superposed and reconstructed to produce the final prediction result.The dust concentration data from a working face in the Sandaogou coal mine were used to analyze the effects of the constraint factorαon the VMD decomposition performance and the number of modes K on the prediction performance.The analysis results showed that:when K=5,the samples were completely decomposed by VMD,and each mode component contained detailed frequency information,allowing for a clear and intuitive analysis of the overall signal's composition;whenα=2000,the profiles of each mode component were complete and fully separated,whereas an excessively smallαled to more redundant information in the independent components,and as the value ofαincreased,the bandwidth of the mode components continuously decreased while the resolution improved.The experimental results showed that:with K=5 andα=2000,the error between the VMD-LSTM's predicted results and the measured values was minimal,and its MAE,MSE,RMSE,and MRE were all superior to those of other models.The VMD-LSTM model exhibits strong generalization ability and robustness for predicting nonlinear,non-stationary,and high-noise dust concentrations under complex environmental conditions.
作者
李永中
陈博
王海山
胡世奇
王攀
郑谐
LI Yongzhong;CHEN Bo;WANG Haishan;HU Shiqi;WANG Pan;ZHENG Xie(CHN Energy Digital Intelligence Technology Development(Beijing)Co.,Ltd.,Beijing 100011,China;Sandaogou Coal Mine,Shaanxi Deyuan Fugu Energy Co.,Ltd.,Fugu 719400,China;CCTEG Chongqing Research Institute,Chongqing 400039,China)
出处
《工矿自动化》
北大核心
2025年第9期90-97,156,共9页
Journal of Mine Automation
基金
国家重点研发计划项目(2023YFC2509305)
重庆市自然科学基金项目(CSTB2023NSCQ-MSX0736)。
关键词
矿井粉尘浓度预测
变分模态分解
长短期记忆网络
模态数量
约束因子
VMD-LSTM
mine dust concentration prediction
variational mode decomposition
long short-term memory network
number of modes
constraint factor
VMD-LSTM
