Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment.

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Auroral events are the prominent manifestation of solar/stellar forcing on planetary atmospheres because they are closely related to the stellar energy deposition by and evolution of planetary atmospheres. A numerical kinetic Monte Carlo model was developed with the aim to calculate the steady-state energy distribution functions of suprathermal N(4S) atoms in the polar upper atmosphere formed due to the precipitation of high-energy auroral electrons in the N2-O2 atmospheres of rocky planets in solar and exosolar planetary systems. This model describes on the molecular level the collisions of suprathermal N(4S) atoms and atmospheric gas taking into account the stochastic nature of collisional scattering at high kinetic energies. It was found that the electron impact dissociation of N2 is an important source of suprathermal N atoms, significantly increasing the non-thermal production of nitric oxide in the auroral regions of the N2-O2 atmospheres of terrestrial-type planets.