The paper describes an original technique for the real-time monitoring of parameters and technical diagnostics of small unmanned aerial vehicle (UAV) units using neural network models with the proposed CompactNeuroUAV architecture. As input data, the operation parameter values for a certain period preceding the current and actual control actions on the UAV actuators are used. A reference parameter set model is trained based on historical data. CompactNeuroUAV is a combined neural network consisting of convolutional layers to compact data and recurrent layers with gated recurrent units to encode the time dependence of parameters. Processing provides the expected parameter value and estimates the deviation of the actual value of the parameter or a set of parameters from the reference model. Faults that have led to the deviation threshold crossing are then classified. A smart classifier is used here to detect the failed UAV unit and the fault or pre-failure condition cause and type. The paper also provides the results of experimental validation of the proposed approach to diagnosing faults and pre-failure conditions of fixed-wing type UAVs for the ALFA dataset. Models have been built to detect conditions such as engine thrust loss, full left or right rudder fault, elevator fault in a horizontal position, loss of control over left, right, or both ailerons in a horizontal position, loss of control over the rudder and ailerons stuck in a horizontal position. The results of estimating the developed model accuracy on a test dataset are also provided.

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