Transformer based time series prediction method for mine internal caused fire
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摘要: 传统的基于机器学习的矿井内因火灾预测方法尽管具备一定的预测能力,然而在处理复杂的多变量数据时不能有效捕捉数据间的全局依赖关系,导致预测精度较低。针对上述问题,提出了一种基于Transformer的矿井内因火灾时间序列预测方法。首先,采用Hampel滤波器和拉格朗日插值法对数据进行异常值检测和缺失值填补。然后,利用Transformer的自注意力机制对时间序列数据进行特征提取及趋势预测。最后,通过调节滑动窗口的大小与步长,在不同的时间步长和预测长度下对模型进行不同时间维度的训练。结合气体分析法将矿井火灾产生的标志性气体(CO,O2,N2,CO2,C2H2,C2H4,C2H6)作为模型输入变量,其中CO作为模型输出的目标变量,O2,N2,CO2,C2H2,C2H4,C2H6作为模型输入的协变量。选取陕煤集团柠条塔煤矿S1206回风隅角火灾预警的束管数据进行实验验证,结果表明:① 对CO进行单变量预测和多变量预测,多变量预测相比单变量预测有着更高的预测精度,说明多变量预测能通过捕捉序列间的相关性提高模型的预测精度。② 当时间步长固定时,基于Transformer的矿井内因火灾预测模型的预测精度随着预测长度的增加而下降。当预测长度固定时,模型的预测精度随时间步长增加而提高。③ Transformer算法的预测精度较长短时记忆(LSTM)算法和循环神经网络(RNN)算法分别提高了7.1%~12.6%和20.9%~24.9%。
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关键词:
- 矿井内因火灾 /
- Transformer /
- 时间序列 /
- 标志性气体 /
- 自注意力机制
Abstract: Although traditional machine learning based methods for predicting mine internal caused fire have certain predictive capabilities, they cannot effectively capture global dependencies between complex multivariate data, resulting in low prediction precision. In order to solve the above problems, a transformer based time series prediction method for mine internal caused fire is proposed. Firstly, the Hampel filter and Lagrange interpolation method are used to detect outliers and fill in missing values in the data. Secondly, the self attention mechanism of Transformer is utilized to extract features and predict trends from time series data. Finally, by adjusting the size and step size of the sliding window, the model is trained in different time dimensions at different time steps and prediction lengths. Combining gas analysis method, the iconic gases generated by mine fires (CO, O2, N2, CO2, C2H2, C2H4, C2H6) are used as input variables for the model, with CO as the target variable for model output and O2, N2, CO2, C2H2, C2H4, C2H6 as covariates for model input. Selecting the bundle data of S1206 return air corner fire warning in Ningtiaota Coal Mine of Shanmei Coal Group for experimental verification, the results show the following points. ① Univariate prediction and multivariate prediction of CO show that multivariate prediction has higher prediction precision than univariate prediction, indicating that multivariate prediction can improve the prediction precision of the model by capturing the correlation between sequences. ② When the time step is fixed, the prediction precision of the Transformer based mine internal caused fire prediction model decreases with the increase of prediction length. When the prediction length is fixed, the prediction precision of the model improves with the increase of time step. ③ The prediction accuracy of the Transformer algorithm is improved by 7.1%-12.6% and 20.9%-24.9% over the long short-term memory (LSTM) algorithm and recurrent neural network (RNN) algorithm, respectively.-
Key words:
- mine internal caused fire /
- Transformer /
- time series /
- iconic gas /
- self attention mechanism
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表 1 基于 Transformer的矿井内因火灾预测模型在不同时间步长下的误差
Table 1. Errors of mine internal caused fire prediction model based on Transformer under different time dimensions
预测长度 时间步长20 时间步长30 时间步长40 MAE RMSE MAE RMSE MAE RMSE 5 0.012 3 0.0015 0.0119 0.0147 0.0113 0.0139 10 0.012 7 0.0160 0.0124 0.0156 0.0119 0.0144 15 0.014 1 0.0183 0.0126 0.0161 0.0128 0.0166 表 2 不同算法预测结果比较
Table 2. Comparison of the prediction results of the different algorithms
时间步长 MAE Transformer LSTM RNN 20 0.0160 0.0183 0.0213 30 0.0156 0.0178 0.0200 40 0.0144 0.0155 0.0182 -
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