Mine Gas Concentration Pre-Warning Based Monitoring Data Relational Analysis

2013 ◽  
Vol 634-638 ◽  
pp. 3655-3659 ◽  
Author(s):  
Ding Wen Dong ◽  
Hong Gang Wang ◽  
Peng Tao Jia
Keyword(s):  
Monitoring Data ◽  
Statistical Characteristics ◽  
Gas Monitoring ◽  
Gas Concentration ◽  
Relational Analysis ◽  
Safe Management ◽  
Intrinsic Correlation ◽  
Mine Gas ◽  
Monitoring Data Processing ◽  
Grey Relational

For efficient analysis of mine gas monitoring data to expand monitor system function and realize effective gas pre-warning, the gas concentration pre-warning method based on monitoring data processing was studied. Studying by mine gas monitoring data, its statistical characteristics was abstracted, the intrinsic correlation characteristics of time series consisted by gas monitoring data was also analyzed by using grey relational analysis method. Further, the pre-warning indexes and its thresholds were determined, and the gas concentration abnormal situations could be analyzed, which realized timely and dynamically quantitative pre-warning. The case analysis shows that the method has a better applicability for mine-site gas concentration pre-warning, which can offer the effective decision for daily safe management.

Study on Coal Mine Gas Monitoring System Based on Arduino

2014 ◽  
Vol 1073-1076 ◽  
pp. 2173-2176 ◽  
Author(s):  
Hui Chun Gao ◽  
Chao Jun Fan ◽  
Jun Wen Li ◽  
Ming Kun Luo
Keyword(s):  
Monitoring System ◽  
Coal Mine ◽  
Low Cost ◽  
Gas Monitoring ◽  
Underground Coal Mine ◽  
Gas Concentration ◽  
Coal Mine Gas ◽  
Good Market ◽  
Mine Gas ◽  
Sd Card

Aimed at the frequency gas accident of coal mine, we designed a coal mine gas monitoring system based on Arduino microcontroller. The MQ-4 gas sensor was used to collect gas concentration, wireless ZigBee was used to transfer data of gas concentration to PC. The system can display gas concentration real-timely by LCD and use SD card to store the data. The system will send out sound and light alarm when the gas concentration overruns. Industrial tests have been carried out in Wuyang coal mine. Results show that gas monitoring system can well adapt to environment of underground coal mine and the measurement is accurate. The system is real-time monitoring and early warning. It has the characteristics of low power consumption, low cost, wireless, good market prospect.

scholarly journals Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 422 ◽  
Author(s):  
Bing Zeng ◽  
Jiang Guo ◽  
Fangqing Zhang ◽  
Wenqiang Zhu ◽  
Zhihuai Xiao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Ambient Temperature ◽  
Grey Relational Analysis ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Dissolved Gas ◽  
Gas Concentration ◽  
Relational Analysis ◽  
Mode Decomposition ◽  
Grey Relational

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

Research on Mine Gas Prediction Based on Cloud Computing Data Integrating Mode

2013 ◽  
Vol 849 ◽  
pp. 435-440
Author(s):  
Yi Zhang
Keyword(s):  
Cloud Computing ◽  
Prediction Method ◽  
Monitoring Data ◽  
Time Data ◽  
Gas Monitoring ◽  
Data Simulation ◽  
Simulation Based ◽  
Source Data ◽  
Mine Gas ◽  
Chinese Coal

Because of the lack of data sharing mechanism among different safety monitor systems used in current Chinese coal mine enterprises, it is unlikely to fuse and process mine gas monitoring data and human monitoring data. In this article, a mine gas prediction method is proposed based on cloud computing data integrating mode. The method integrates all mine gas monitoring data server resources together, and then processes the obtained data by using network distributed computing resources cluster pattern and analysis the processed data. The analyzed result will provide technique support for decision making. The article introduced the architecture of cloud computing data integrating mode pattern, and built up simulation based on real time data. Simulation result indicated that it is sufficient and accurate to predict mine gas density by using virtual service system to achieve multi-source data calculation.

Research of Coal Mine Gas Monitoring System Based on Technology Fusion of Wireless Sensor Network and 3DGIS

2011 ◽  
Vol 314-316 ◽  
pp. 2263-2267 ◽  
Author(s):  
Xiang Yun Wan ◽  
Hao Yang ◽  
Lan Hui Sun ◽  
Hao Min Tian ◽  
Zhan Feng Huang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Monitoring System ◽  
Coal Mine ◽  
Wireless Sensor ◽  
Gas Monitoring ◽  
Gas Concentration ◽  
Coal Mine Gas ◽  
Gas Disaster ◽  
Mine Gas

At present, gas disaster is one of the main problems for Chinese coal industry. The prevention and control of gas accident is an important part of coal mine enterprise production safety. This paper uses wireless sensor network to collect the coal mine gas concentration parameter, transmits and stores the data to the server of gas monitoring system by information fusion of sink, and synchronously displays the data to the client in the state of three-dimensional space. When the gas concentration of monitored coal mine roadway is greater than the limit, the sound and light alarm will be trigged and the alarm area will be displayed. In the meantime, the information of contingency plan will be provided. The system gives a strong technical support for the early warning and controlling of gas disaster and provides a reference for the prevention of gas accidents in coal enterprises.

Analysis of Influencing Factors on Mine Gas Emissions Based on Grey Correlation

2013 ◽  
Vol 756-759 ◽  
pp. 4525-4528
Author(s):  
Xiao Yan Tang ◽  
Liu Ke ◽  
Pei Yun Wang
Keyword(s):  
Statistical Data ◽  
Analysis Method ◽  
Gas Emission ◽  
Gas Emissions ◽  
Relational Analysis ◽  
Control Factors ◽  
Relational Degree ◽  
The Index System ◽  
Mine Gas ◽  
Grey Relational

Statistical analysis for the data of HC mine gas emission rate by using grey relational analysis method, the index system effecting on mine gas emission factors are established with the method and the relational degrees between the factors and mine gas emissions are analyzed. The main factors effecting on mine gas emission are determined based on the size of relational degree by the quantitative analysis. The results show that the method is simple, and provides more flexibility to comprehensive analysis of the statistical data, and overcome the shortcomings of a different dimension data can not find regular. Its useful to find the main control factors effecting on mine gas emission.

scholarly journals The Grey Relational Analysis of Sluice Monitoring Data

2011 ◽  
Vol 15 ◽  
pp. 5192-5196 ◽  
Author(s):  
Yu Guoqing ◽  
Wang Zili ◽  
Zhang Baosen ◽  
Xie Zhigang
Keyword(s):  
Grey Relational Analysis ◽  
Monitoring Data ◽  
Relational Analysis ◽  
Grey Relational
Sign in / Sign up

Export Citation Format

Share Document