智能通风矿井风速传感器数据清洗模型

doi:10.16265/j.cnki.issn1003-3033.2023.09.1032

摘要/Abstract

摘要：

针对当前智能通风矿井风速传感器监测数据清洗破坏信息完整性等问题,提出一种基于堆叠降噪自编码器(SDAE)的矿井风速传感器监测数据清洗模型。首先应用通风系统正常运行状态下的风速数据样本进行SDAE训练,并基于核密度估计(KDE)方法获取训练样本的重构误差上限及容限时间;然后分析测试样本中重构误差、误差持续时间与训练样本的重构误差上限、容限时间之间的关系,辨别“脏”数据类型;最后利用东山煤矿风速传感器监测数据进行有故障样本和无故障样本的数据清洗试验。结果表明:所提模型能自动辨别噪声点和缺失值,并通过数据重构修复“脏”数据,在过滤干扰数据的同时可有效保留通风故障状态信息,相比于降噪自编码器(DAE)、长短时记忆(LSTM)神经网络和卡尔曼滤波(KF)等其他数据清洗模型,该模型的平均绝对误差(MAE)和均方根误差(RMSE)平均降低了75.42%和74.98%。

关键词: 矿井通风, 风速传感器, 数据清洗, 数据重构, 堆叠降噪自编码器(SDAE)

Abstract:

At present, there are some problems such as incomplete data and information loss in monitoring data cleaning of mine airflow speed sensor. Therefore, a data cleaning method for mine airflow speed sensor based on SDAE was proposed. The airflow speed data samples of the ventilation system under normal conditions were trained by the SDAE algorithm to obtain the upper limit of reconstruction error and tolerance time by kernel density estimation (KDE). By comparing reconstruction error and error duration of test samples with the upper limit of reconstruction error and tolerance time, the "dirty" data was resolved. Using the monitoring data of wind speed sensor in Dongshan coal mine, the data cleaning test of faulty samples and non-faulty samples was carried out. The results show that this method can automatically identify the noise points and missing values. The "dirty" data is repaired directly by reconstruction. In the case of ventilation system fault, this method can effectively retain the state information while filtering out the interference data. Compared with other data cleaning models such as denoising autoencoder (DAE), long short-term memory (LSTM) neural network, and Kalman filter (KF), the average mean absolute error (MAE) and root mean squared error (RMSE) values of this model are reduced by 75.42% and 74.98% respectively.

Key words: mine ventilation, speed sensor, data cleaning, data reconstruction, stacked denoising autoencoder(SDAE)

赵丹, 沈志远, 宋子豪, 解丽娜, 刘柏辰. 智能通风矿井风速传感器数据清洗模型[J]. 中国安全科学学报, 2023, 33(9): 56-62.

ZHAO Dan, SHEN Zhiyuan, SONG Zihao, XIE Li'na, LIU Baichen. Mine airflow speed sensor data cleaning model for intelligent ventilation[J]. China Safety Science Journal, 2023, 33(9): 56-62.

图/表 9

图1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 18

[1]	周福宝, 魏连江, 夏同强, 等. 矿井智能通风原理、关键技术及其初步实现[J]. 煤炭学报, 2020, 45(6):2225-2235.
	ZHOU Fubao, WEI Lianjiang, XIA Tongqiang, et al. Principle, key technology and preliminary realization of mine intelligent ventilation[J]. Journal of China Coal Society, 2020, 45(6):2225-2235.
[2]	张巨峰, 施式亮, 鲁义, 等. 大数据下瓦斯与煤自燃共生灾害智能预警系统:数据特征、应用架构、关键技术[J]. 中国安全科学学报, 2021, 31(9):60-66. doi: 10.16265/j.cnki.issn1003-3033.2021.09.009
	ZHANG Jufeng, SHI Shiliang, LU Yi, et al. Intelligent early warning system of gas and coal spontaneous combustion disaster based on big data: data characteristics, application structure and key technologies[J]. China Safety Science Journal, 2021, 31(9):60-66. doi: 10.16265/j.cnki.issn1003-3033.2021.09.009
[3]	WANG Guofa, XU Yongxiang, REN Huaiwei. Intelligent and ecological coal mining as well as clean utilization technology in China: review and prospects[J]. International Journal of Mining Science and Technology, 2019, 29(2):161-169. doi: 10.1016/j.ijmst.2018.06.005
[4]	赵丹, 潘竞涛. 改进灵敏度矩阵的矿井通风故障源诊断及传感器布置研究[J]. 中国安全科学学报, 2011, 21(2):78-84.
	ZHAO Dan, PAN Jingtao. Fault source diagnosis for mine ventilation based on improved sensitivity matrix and its wind speed[J]. China Safety Science Journal, 2011, 21(2):78-84.
[5]	赵丹, 沈志远, 刘晓青. 基于OCISVM的矿井通风系统在线故障诊断[J]. 中国安全科学学报, 2022, 32(10):76-82. doi: 10.16265/j.cnki.issn1003-3033.2022.10.1766
	ZHAO Dan, SHEN Zhiyuan, LIU Xiaoqing. Online fault diagnosis of mine ventilation system based on OCISVM[J]. China Safety Science Journal, 2022, 32(10) :76-82. doi: 10.16265/j.cnki.issn1003-3033.2022.10.1766
[6]	MUDULI L, MISHRA D P, JANA P K. Application of wireless sensor network for environmental monitoring in underground coal mines: a systematic review[J]. Journal of Network and Computer Applications, 2018, 106:48-67. doi: 10.1016/j.jnca.2017.12.022
[7]	HUANG De, LIU Jian, DENG Lijun. A hybrid-encoding adaptive evolutionary strategy algorithm for windage alteration fault diagnosis[J]. Process Safety and Environmental Protection, 2020, 136:242-252. doi: 10.1016/j.psep.2020.01.037
[8]	JIA Jinzhang, JIA Peng, LI Bin. Theoretical study on stability of mine ventilation network based on sensitivity analysis[J]. Energy Science & Engineering, 2020, 8(8):2823-2830.
[9]	严英杰, 盛戈皞, 陈玉峰, 等. 基于时间序列分析的输变电设备状态大数据清洗方法[J]. 电力系统自动化, 2015, 39(7):138-144.
	YAN Yingjie, SHENG Gehao, CHEN Yufeng, et al. Cleaning method for big data of power transmission and transformation equipment state based on time sequence analysis[J]. Automation of Electric Power System, 2015, 39(7):138-144.
[10]	高正男, 杨帆, 胡姝博, 等. 面向新能源电力系统状态估计的伪波动数据清洗[J]. 高电压技术, 2022, 48(6):2366-2377.
	GAO Zhengnan, YANG Fan, HU Shubo, et al. Pseudo-fluctuation data cleaning for state estimation of new energy power system[J]. High Voltage Engineering, 2022, 48(6):2366-2377.
[11]	HINTON G, SALAKHUTDINOV R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313:504-507. doi: 10.1126/science.1127647 pmid: 16873662
[12]	VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising antoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12):3371-3408.
[13]	XU Fan, YANG Fangfang, FEI Zicheng, et al. Life prediction of lithium-ion batteries based on stacked denoising autoencoders[J]. Reliability Engineering & System Safety, 2021, 208:1-14.
[14]	DAI Jiejie, SONG Hui, SHENG Gehao, et al. Cleaning method for status monitoring data of power equipment based on stacked denoising autoencoders[J]. IEEE Access, 2017, 5:22 863-22 870. doi: 10.1109/ACCESS.2017.2740968
[15]	HUANG De, LIU Jian, DENGE Lijun, et al. An adaptive Kalman filter for online monitoring of mine wind speed[J]. Archives of Mining Sciences, 2019, 64(4):813-827. doi: 10.24425/ams.2019.131068
[16]	张巍, 李雨成, 张欢, 等. 面向通风智能化的风速传感器结构化数据降噪方法对比[J]. 中国安全生产科学技术, 2021, 17(8):70-76.
	ZHANG Wei, LI Yucheng, ZHANG Huan, et al. Comparison of structured data noise reduction methods for airflow speed sensor of intelligent ventilation[J]. Journal of Safety Science and Technology, 2021, 17(8):70-76.
[17]	屈世甲. 巷道风速传感器数据实时处理方法[J]. 煤矿安全, 2017, 48(2):163-166.
	QU Shijia. Real-time date processing method of wind speed sensor in roadway[J]. Safety in Coal Mines, 2017, 48(2):163-166.
[18]	LU Dan, BAOZhen, LIZuyi. Load sampling for SCUC based on principal component analysis and kernel density estimation[C]. 2016 IEEE Power and Energy Society General Meeting (PESGM), IEEE, 2016:1-5.