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Establishment and Application of Coalmine Gas Prediction Model Based on Multi-Sensor Data Fusion Technology

Undoubtedly an accident involving gas is one of the greater disasters that can occur in a coalmine, thus being able to predict when an accident involving gas might occur is an essential aspect in loss prevention and the reduction of safety hazards. However, the traditional methods concerning gas safety prediction is hindered by multi-objective and non-continuous problem. The coalmine gas prediction model based on multisensor data fusion technology (CGPM-MSDFT) was established through analysis of accidents involving gas using artificial neural network to fuse multi-sensor data, using an improved algorithm designed to train the network and using an early stop method to resolve the over-fitting problem, the network test and field application results show that this model can provide a new direction for research into predicting the likelihood of a gas related incident within a coalmine. It will have a broad application prospect in coal mining. Copyright © 2014 IFSA Publishing, S. L.