Study On Multi-Sensor Information Fusion And Its Application On The Gas Outburst Prediction System

Gas outbursts in mines are among the main disasters in China. Gas outburst prediction is a complex theoretical and technical project, as the phenomenon of gas outbursts results from multiple effects of a geological, physical and chemical nature, and there is a lack of effective monitoring methods for this disaster. For resolving the seeminlgy unpredictable and non-linear problem of gas outbursts, a new model of a gas outburst prediction system based on multi-sensor information fusion is put forward, and the data level, feature level and decision level is analysed and studied respectively. And an intelligent decision prediction system is presented, the example results of this system show that it could significantly increase the accuracy of gas outburst prediction.After summarizing the general architecture of multi-sensor information fusion, this work puts forward a new model of a gas outburst prediction system. It is based on multi-sensor information fusion, which is described as hierarchical fusion in the feature fusion level and the decision level. And it is a closed contrl model through the feedback of sensor management system.Based on analysing the current indexes and their critical values which were put forward by different researchers, this thesis presents a hierarchical analysis method for arranging the main typical influencing factors in the order of their significance and chooses the most suitable sensors according to the most important factors. After analysing the limitations of local static sensors, a real-time dynamic sensor is studied using the active olfaction technology.As the merits and demerits of each multi-sensor information fusion method, the Artificial Neural Network (ANN) is chosen as feature fusion algorithm. Three improved BP neural network could improve the learning velocity and capability of convergence of traditional BP neural network, which is validated by example data analysis of gas outbursts. As the inherent disadvantages of ANN, the Dempster-Shafter (D-S) evidence theory is presented for decision fusion, which could increase the credibility of the decision-making.As one of the more effective uncertain reasoning methods among the information fusion methods, the D-S evidence theory has proven to be a good decision fusion algorithm. This work sums up the main deficiencies of the D-S evidence theory, and gives the corresponding solutions. It puts forward new combination rules of the D-S Evidence Theory for resolving the problem of evidence conflicts based on introducing the distance between the evidence, the credibility of evidence sources, and other notions, and also provides the mathematical proofs for these rules. The improved rules allocate the conflicts to various focal elements according to the credibility of the coherence evidence. The AND-algorithm is adopted to combine the coherence evidence, which reflects the intersection of focus elements. The corresponding mathematical and theoretical analysis proves that the given rules are rational and effective for both highly conflicting and coherent evidence, and that they can deal with conflicting information from different sensors.For combing the D-S evidence theory and the fuzzy set theory, typical current methods and conclusions of extending the evidence theory to fuzzy sets are summarized. Pointing out the disadvantages of previous methods, this paper introduces a new definition of the similarity degree between fuzzy sets, and also a decision-making fusion algorithm which can combine fuzzy evidence effectively.Mathematical proofs and example analyses validate the new algorithm and demonstrate that it is more effective, acquires more information on the change of fuzzy focal elements, and produces better fusion results than existing methods. In order to validate the model of the gas outburst prediction system based on multi-sensor information presented in this thesis, the gas outburst data of five typical mines are chosen. According to the improved combination rules of the D-S evidence theory, an intelligent prediction system of gas outbursts is developed on a Windows Vista +Visual Studio 2008 software platform which provides real-time decision support for underground workers in coal mines. The results from the analysis of our gas outburst multi-regular decision fusion system show that the system supplies accurate and credible disaster prediction, which effectively improves the level of coal mine safety management.