Study On Multi-Sensor Information Fusion And Knowledge Discovery On The Coal Mine Gas Monitoring

In coal mine area, the most crucial safety issue is still gas disaster prevention and control.Based on coal-gas outburst and gas explosion prediction technology and early warning theory, this thesis aimed at using MSIF method to mine regular knowledge from data about gas concentration, wind speed, electromagnetic radiation, and acoustic emission, et al. In addition to that, following the principle of diverse methodologies, complementary advantages, cross validation and short-long term integration, this thesis also has built a coal-gas safety monitoring and early-warning system based on fusion of multi-sensors’data. This system could accurately monitor the mining face’s coal-gas safety in real time and rapidly provide effective control measures. The main research achievements of this dissertation are as follows:A solid foundation in understanding theory of coal-gas outburst warning by using multi-sensors’data was established based on literature review of coal-gas safety dynamic monitoring methods and outburst risk evaluation indexes which include gas concentration, electromagnetic radiation and acoustic emission, et al.After field investigation and analysis of the system actual demands, the final structure of coal-gas monitoring sensors’data fusion system was proposed based on the consideration of enhancing efficiency of the monitoring system and reducing its resources consumption, and the research focused on coal-gas outburst prediction fuzzy expert system which makes decision based on fusion of multi-sensors’data.Gas monitoring warning signal identification method based on time series similarity measure is presented. The representative gas warning time series (GWTS) patterns are gained by clustering analysis method based on DTW distance. With the piecewise morphological measure methods, the character value table is established.Based on spatio-temporal correlation analysis method, the automatic identification techniques for coal-gas monitoring data anomaly are presented.The coal-gas safety monitoring problem was classified as an expert diagnosis issue here. The methods of knowledge discovery and expert knowledge base system design based on cognitive model are provided.