Distributed gas concentration prediction with intelligent edge devices in coal mine
Distributed gas concentration prediction with intelligent edge devices in coal mine
复制标题
煤矿智能边缘设备分布式瓦斯浓度预测
DOI：
10.1016/j.engappai.2020.103643
复制
发表时间：
2020-06
影响因子：
8
通讯作者：
Patrick C.K. Hung
中科院分区：
文献类型：
--
作者：
Zhang Yiwen;Guo Haishuai;Lu Zhihui;Lu Zhan;Patrick C.K. Hung
瓦斯浓度超标会引发瓦斯灾害，瓦斯浓度预测系统通过预测瓦斯浓度的变化趋势，及时提醒工程人员采取必要的措施，减少瓦斯灾害的发生。随着智能边缘设备在煤矿中的应用越来越多，以及现有系统的局限性，开发一种新型的适用于大型智能边缘设备的瓦斯浓度预测系统成为一个重要课题。这项工作提出了通过一种新的方法来解决这个问题，该方法通过充分利用智能边缘系统中的多维数据来预测气体浓度。具体来说，1）提出了一种基于区间预测而非点预测的单隐层随机权值神经网络（SRWNN）作为预测模型，2）采用非支配排序遗传算法II（NSGA-II）训练SRWNN; 3）为了显著减少模型训练期间消耗的时间并促进实时预测，提出了一种基于智能边缘系统的分布式瓦斯浓度预测方案; 4）利用从某公司采集的实际工业数据进行了大量的实验，以证明所提出的方法的上级性能。
Gas disaster can be triggered by gas concentrations exceeding standard levels, and gas concentration prediction system can reduce the occurrence of gas disaster by predicting the trend of gas concentration and alerting engineers to take necessary measures whenever needed. With the increasing use of intelligent edge devices in coal mines and the limitations of some existing systems, developing a new gas concentration prediction system for large-scale intelligent edge devices has become an important issue. This work proposes to address the issue through a novel method for predicting gas concentrations by taking full advantage of multidimensional data in an intelligent edge system. Specifically, 1) it proposed aSingle hidden layerRandomWeightsNeuralNetwork (SRWNN) as the prediction model, which is based on interval prediction rather than point prediction; 2) It employs a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to train SRWNN; 3) To significantly reduce the time consumed during model training and facilitate real-time predictions, it proposes a distributed gas concentration prediction scheme based on an intelligent edge system; and 4) it conducts extensive experiments by using actual industrial data collected from a company to demonstrate the superior performance of the proposed method.
登录
查看更多内容
影响因子：
9.6
作者：
Yue Xiang;Junyong Liu;Yilu Liu
通讯作者：
Yue Xiang;Junyong Liu;Yilu Liu
影响因子：
6
作者：
Huang, Guang-Bin;Zhu, Qin-Yu;Siew, Chee-Kheong
通讯作者：
Siew, Chee-Kheong
DOI：
10.1109/tase.2018.2844204
发表时间：
2019-04
影响因子：
5.6
作者：
Yuxin Wen;Jianguo Wu;Qiang Zhou;T. Tseng
通讯作者：
Yuxin Wen;Jianguo Wu;Qiang Zhou;T. Tseng
影响因子：
6.6
作者：
Tsai, Men-Shen;Hsu, Fu-Yuan
通讯作者：
Hsu, Fu-Yuan
DOI：
10.1016/j.neunet.2013.02.012
发表时间：
2013-09
期刊：
Neural networks : the official journal of the International Neural Network Society
影响因子：
--
作者：
Cheng-Yi Liu;Chein Chen;Ching-Ter Chang;Lun-Min Shih
通讯作者：
Cheng-Yi Liu;Chein Chen;Ching-Ter Chang;Lun-Min Shih