基于改进门控循环神经网络的采煤机滚筒调高量预测

齐爱玲; 王雨; 马宏伟

doi:10.13272/j.issn.1671-251x.2023110039

基于改进门控循环神经网络的采煤机滚筒调高量预测

doi: 10.13272/j.issn.1671-251x.2023110039

齐爱玲^1,,
王雨^1, ,,
马宏伟²

1.
西安科技大学计算机科学与技术学院，陕西西安　710054
2.
西安科技大学机械工程学院，陕西西安　710054

基金项目: 国家自然科学基金资助项目(61674121)。

详细信息

作者简介:
齐爱玲（1972—），女，陕西西安人，副教授，博士，研究方向为煤岩识别、信号与图像处理，E-mail：qalemail@126.com

通讯作者:
王雨(2000—)，女，陕西榆林人，硕士研究生，研究方向为煤岩预测，E-mail: 21208223078@stu.xust.edu.cn。

中图分类号: TD421.6
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出版历程
- 收稿日期: 2023-11-13
- 修回日期: 2024-02-19
- 网络出版日期: 2024-03-04

Prediction of height adjustment of shearer drum based on improved gated recurrent neural network

QI Ailing^1
,,
WANG Yu^{1
, ,},
MA Hongwei²

1.
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China
2.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

摘要

摘要: 采煤机自适应截割技术是实现综采工作面智能化开采的关键技术。针对采煤机在复杂煤层下自动截割精度较低的问题，提出了一种基于改进门控循环神经网络（GRU）的采煤机滚筒调高量预测方法。鉴于截割轨迹纵向及横向相邻数据之间的相关性，采用定长滑动时间窗法对获取的采煤机滚筒高度数据进行预处理，将输入数据划分为连续、大小可调的子序列，同时处理横向、纵向的特征信息。为提高模型预测效率，满足循环截割的实时性要求，提出了一种用因果卷积改进的门控循环神经网络（CC−GRU），对输入数据进行双重特征提取和双重数据过滤。CC−GRU利用因果卷积提前聚焦序列纵向的局部时间特征，以减少计算成本，提高运算速度；利用门控机制对卷积得到的特征进行序列化建模，以捕捉元素之间的长期依赖关系。实验结果表明，采用CC−GRU模型对采煤机滚筒调高量进行预测，平均绝对误差（MAE）为43.80 mm，平均绝对百分比误差（MAPE）为1.90%，均方根误差（RMSE）为50.35 mm，决定系数为0.65，预测时间仅为0.17 s；相比于长短时记忆（LSTM）神经网络、GRU、时域卷积网络（TCN），CC−GRU模型的预测速度较快且预测精度较高，能够更准确地对采煤机调高轨迹进行实时预测，为工作面煤层模型的建立和采煤机调高轨迹的预测提供了依据。
- 采煤机 /
- 滚筒调高 /
- 煤岩识别 /
- 深度学习 /
- 门控循环神经网络 /
- 因果卷积
Abstract: The adaptive cutting technology of the shearer is a key technology for achieving intelligent mining in fully mechanized working faces. In order to solve the problem of low automatic cutting precision of the shearer in complex coal seams, a prediction method for the height adjustment of shearer drum based on improved gated recurrent neural network (GRU) is proposed. Considering the correlation between adjacent data in the longitudinal and transverse directions of the cutting trajectory, the fixed length sliding time window method is used to obtain the height data of the shearer drum. The input data is divided into continuous and adjustable subsequences, while processing the feature information in the transverse and longitudinal directions. To improve the prediction efficiency of the model and meet the real-time requirements of cyclic cutting, causal convolution gated recurrent unit(CC-GRU) is proposed to perform dual feature extraction and dual data filtering on input data. CC-GRU utilizes causal convolution to focus on the local temporal features in the longitudinal direction of the sequence in advance, in order to reduce computational costs and improve computational speed. CC-GRU uses gating mechanism to serialize and model the features obtained from convolution to capture long-term dependencies between elements. The experimental results show that using the CC-GRU model to predict the height adjustment of the shearer drum, the MAE is 43.80 mm, MAPE is 1.90%, RMSE is 50.35 mm, the determination coefficient is 0.65, and the prediction time is only 0.17 seconds. Compared to long short term memory (LSTM) neural networks, GRU, and temporal convolutional network (TCN), the CC-GRU model has a faster prediction speed and higher prediction precision. It can more accurately predict the height adjustment trajectory of the shearer in real time. This provides a basis for the establishment of coal seam models in the working face and the prediction of the height adjustment trajectory of the shearer.
- shearer /
- drum height adjustment /
- coal rock recognition /
- deep learning /
- gated recurrent neural network /
- causal convolution

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图 1 基于CC−GRU的采煤机滚筒调高量预测流程

Figure 1. Prediction process of height adjustment of shearer drum based on causal convolution gated recurrent unit （CC-GRU）

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图 2 CC−GRU预测模型结构

Figure 2. Structure of CC-GRU prediction model

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图 3 扩张因果卷积结构

Figure 3. Causal dilated convolutional structure

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图 4 GRU结构

Figure 4. Structure of gated recurrent unit

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图 5 插值曲面

Figure 5. Interpolation surface

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图 6 不同参数下CC−GRU模型的预测结果对比

Figure 6. Comparison of prediction results of CC-GRU model under different parameters

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图 7 不同模型预测结果对比

Figure 7. Comparison of prediction results of different models

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图 8 评价指标对比

Figure 8. Comparison of evaluation indicators

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表 1 不同参数下CC−GRU模型的预测结果

Table 1. Prediction results of CC-GRU model under different parameters

隐层层数	隐层节点数			$ {\rm{MAE}}/{\mathrm{mm}} $	$ {\rm{MAPE}}/\text{%} $	$ {\rm{RMSE}}/{\mathrm{mm}} $	$ {{{R^{\text{2}}}}} $
隐层层数	第1层	第2层	第3层	$ {\rm{MAE}}/{\mathrm{mm}} $	$ {\rm{MAPE}}/\text{%} $	$ {\rm{RMSE}}/{\mathrm{mm}} $	$ {{{R^{\text{2}}}}} $
2	16	16	—	46.54	2.02	53.41	0.61
2	32	16	—	51.88	2.25	64.73	0.35
2	32	32	—	43.80	1.90	50.35	0.65
2	64	32	—	47.44	2.06	54.38	0.55
2	64	64	—	50.58	2.19	57.40	0.45
3	16	16	16	47.84	2.07	54.15	0.54
3	32	16	16	50.98	2.21	61.70	0.41
3	32	32	16	48.89	2.12	55.86	0.56
3	32	32	32	53.71	2.32	67.33	0.29

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表 2 不同模型评价指标

Table 2. Evaluation indicators of different models

模型	$ {\rm{MAE}}/{\mathrm{mm}} $	$ {\rm{MAPE}}/ {\text{%}} $	$ {\rm{RMSE}}/{\mathrm{mm}} $	$ {{{R^{\text{2}}}}} $
LSTM	51.01	2.21	63.85	0.36
GRU	48.25	2.10	59.58	0.38
TCN	61.27	2.64	66.41	0.32
CC−GRU	43.80	1.90	50.35	0.65

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表 3 不同模型预测时间对比

Table 3. Comparison of prediction time of different models

模型	预测时间/s
LSTM	0.96
GRU	0.88
TCN	0.08
CC−GRU	0.17

下载: 导出CSV

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