基于遗传-深度神经网络的分布式光纤监测工作面矿压预测
DOI:
作者:
作者单位:

1.西安科技大学通信与信息工程学院 西安;2.西安科技大学能源学院

作者简介:

通讯作者:

中图分类号:

TD326.1

基金项目:

国家自然科学基金(51804244);国家重点研发计划项目“互联网+”煤矿安全监管监察关键技术与示范(2018YFC0808301)


Mine Pressure Prediction of Distributed Optical Fiber Monitoring Based on GA-Deep Neural Network on Working Face
Author:
Affiliation:

1.Xi'2.'3.an University of Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan),The National Basic Research Program of China (973 Program)

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    摘要:

    为了准确掌握工作面矿压显现规律并对来压位置进行准确预测,搭建了相似材料物理模拟试验,并引入分布式光纤监测煤层采动过程中上覆岩石变形。结合光纤测点频移值的统计特征及工作面开采位置等矿压影响因素,利用门控循环单元深度神经网络(Gated Recurrent Neural Networks ,GRU),并采用遗传算法(Genetic Algorithm,GA)对GRU网络中的超参数寻优,建立了GA-GRU-BP的工作面来压位置预测模型。采用相关性系数(R2)、平均绝对误差(MAE)和均方根误差(RMSE)作为预测模型的性能评价指标,并与随机森林(RF)、GA-支持向量机(SVM)、GA-GRU进行算法性能比较。实验结果表明, GA-GRU-BP预测模型的预测精度R2达到了98.7%,MAE、RMSE分别为1.224cm,1.769cm, 远低于对比方法的评价指标,表明GA-GRU-BP预测模型具有较高的准确率和鲁棒性,为工作面矿压来压位置预测提供了新的方法。

    Abstract:

    In order to obtain the regulation s of mining pressure and predict the location of the pressure on the working face accurately, a physical simulation test of similar materials was set up, and distributed optical fibers were used to monitor the deformation of the overlying rock during coal mining. Using gated recurrent neural networks, Combining the statistical characteristics of the frequency shift value of the optical fiber sensor and other factors affecting the rock pressure, such as the location of working face, the GA-GRU-BP position prediction model is established on working face. Using Genetic Algorithm (GA) to optimize the hyperparameters during the GRU network training. Correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) are used to evaluate algorithm performance. GA-GRU-BP is compared with random forest (RF), GA-support vector machine(SVM), and GA-GRU. The experimental results show that the R2, MAE, and RMSE of the GA-GRU-BP prediction model are98.7%, 1.224cm, and 1.769cm, respectively, which are all lower than the evaluation indicators of the corresponding comparison method. The GA-GRU-BP position prediction model has a higher accuracy and robustness. A new method for predicting the position of the mining pressure is provided on working face.

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引用本文

冀汶莉,田 忠,张丁丁,等. 基于遗传-深度神经网络的分布式光纤监测工作面矿压预测[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-15
  • 最后修改日期:2022-04-25
  • 录用日期:2022-04-30
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