Development of neural networks to analyze vibration signals of mining equipment and prevent emergency situations

The article discusses the application of neural networks to analyze vibration signals of mining equipment in order to predict and prevent emergencies. A multilayer architecture of neural network trained has been developed using data from vibration sensors of real equipment. The proposed method allows to achieve high accuracy (95%) in classifying vibration patterns corresponding to different states of the equipment, i.e. from normal operation mode to pre-emergency states. Based on the analysis of spectral and temporal characteristics of vibrations, the method provides early (30-120 minutes) warning on development of potential faults. Experimental studies on real data confirm the ability of the neural network approach to reduce the number of emergency downtimes by 40% and the cost of emergency repairs by 25%. The proposed method opens prospects for creation of intelligent systems to ensure safety and efficiency of mining equipment based on predictive analytics.