Evaluation of the effectiveness of the forecasting system for special cases in flight based on the analysis of diagnostic data of the aircraft technological equipment

Abstract

The paper proposes an approach to assessing the effectiveness of the forecasting system for special cases in flight based on the analysis of diagnostic data of the aircraft technological equipment. Prediction of special cases in flight is the main task of parametric diagnostics of aircraft technological equipment. To solve this problem, on-board automated monitoring, diagnostics and control of onboard equipment, unloading and information support of the crew make it possible to measure a large number of parameters of the aircraft technological equipment and obtain arrays of such information in digital form. It is proposed to process the received information using the method of predicting special cases in flight based on the identification of abnormal sequences in the diagnostic data of the aircraft technological equipment. To identify anomalous sequences, it is proposed to use a hybrid stochastic model based on the combination of Markov and production models that use temporal rules to refine the transition probabilities between process states. Due to the inclusion of refining production rules in the model, the probability of describing random processes that are not Markovian increases, and it also becomes possible to integrate a priori expert knowledge into the model, which is very important for predicting special cases in flight. It is proposed to evaluate the effectiveness of the forecasting system by two criteria: the efficiency and reliability of the decisions made by the aircraft crew. In the robot, a simulation was carried out on the simulator of a winding ship A-320 for evaluating the situation as well as taking decisions from the negative inheritance of particular types of problems in the country.