This report considers the creation of a controller intended for reconfiguring the artificial intelligence of robotic vehicles. The functional structure of hardware-reconfigurable digital module for intellectual control of robotic vehicles is proposed and further interaction between its functional modules and remote support center in different situations requiring reconfiguration is concerned. The procedures of self-check and self-testing of the hardware-reconfigurable digital module for intellectual control of mobile space-based robots are described, which are necessary to ensure reliability of reconfiguration.

The paper considers the issues of automatic classification of vibrational states of aircraft engine malfunctions based on the use of convolutional neural network processing of vibrational measurement data presented in spectral form and the knowledge of experts with experience in interpreting spectrograms characterizing the vibrational state of aircraft engines. The developed spectrogram analysis model allows the state monitoring of aircraft engines in automatic mode both during maintenance and in flight operation. The system is able to timely notify technical personnel or crew about the appearance of signs of emergency situations, as well as the type of possible malfunctions. It is shown that the main problem affecting the quality of detection of a potential turbine malfunction is a small sample of data corresponding to malfunctioning states. It is proposed to detect emission anomalies in a small sample by recognizing a modified wavelet transform and neural network clustering, which allows more complete formation of a training sample. The data samples used in training the neural network classifier during the experimental studies were generated on the basis of existing archive files containing complete aperture data from engine vibration sensors and information about malfunctions detected in them.

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The present article is devoted to the development of a method and its software implementation for forecasting the critical states of a turbogenerator and its design elements that arise during starting-up & adjustment works and stopping a turbine. The method is based on a short-term prediction of the image of the spectrogram of vibrations during thermal expansion of the rotor and stator. The dependence of the increase in the vibration level in the spectrum with the failure of the turbogenerator design element is substantiated. The model takes into account the influence of thermal expansion on critical states. The technique of training a deep neural network is given in the classification of thermal influences on the level of vibration while a spectrogram receiving. For machine learning of a neural network in software, a recurrent autoencoder is used. The technique of operation is with a time sequence of spectrograms. To test the model is introduced the concept of semantic quality of clustering. Semantic quality, determined as the degree of correspondence between the information that can be extracted from the obtained cluster structure and the formalized presentation of the user. The interpretation of the results of the discovery of turbine generator defects is presented.

The problems of design and implementation of remotely reconfigurable intelligence for space-based robotic systems and, specifically, mobile robots are highlighted. The classification of reconfiguration technologies, the specifications of remote reconfiguration, the functional structure of remotely reconfigurable intelligence are described. The space-based mobile robot-explorer "Turist" is presented.