The oil&gas enterprises have their own safety concerns list. Poor maintenance of equipment is one of the top safety concerns for many enterprises. There are a lot of methods of increasing of safety, but its shouldn’t be used alone. A complex approach and addition the modern technologies in safety ensuring make possible not only increase the quality but increase the efficiently of production. There are a lot of kinds of quality improving of maintenance of electromechanical equipment, one of them are the augmented reality system. This is a new technology and there are no methods and effective estimation for developing and using this technology in industrial production. Developing the special unified modules of augmented reality for maintenance of electromechanical equipment is the actual task. The aim of this research-to identify possible uses, to estimate the effectiveness in ensuring the safety of production and to develop a way of integrating into typical process control systems of the augmented reality system for maintenance of electromechanical equipment.

The paper discusses the method of identifying the technical condition of induction motors by classifying the energy data coming from the main common power bus. The work shows the simulation results of induction motor operation. The correlation between occurring defects and current diagrams is presented. The developed simulation model is demonstrated. The general algorithm for conducting experiments is described. Five different experiments to develop an algorithm for the classification are conducted: determination of the motors number in operation with different power; determination of the motors number in operation with equal power; determination of the mode and load of induction electric motor; determination of the fault and its magnitude with regard to operation and load of induction motor; determination of the fault and its magnitude with regard to operation and load of induction motor with regard to non-linear load in the flow. The article also presents an algorithm for preprocessing data to solve the classification problem. In addition, the classification results are shown and recommendations for testing and using the classification algorithm on a real object are made.

The article covers the issues of increasing the efficiency of tunneling&cleaning combines “Ural”, widely applied at domestic potash mines. It is pointed out that it is possible to provide rational parameters of the process of destroying potash arrays with cutting tools of the executive bodies of mining combines. This assumption is based on the assessment of the drivers load which will reduce the specific energy consumption for ore extraction and will reduce the number of small unenriched classes in breakage products. The authors substantiate rational regime parameters of the operation of the planetary-disk executive bodies in the Ural combines. A method for automatic control of the tunnel&cleaning combine “Ural” is proposed, which ensures the implementation of the process of destruction of the potash massif with rational cutting parameters on the basis of information on the drivers load.