A method for detecting abnormal sequences in the diagnostic data of technological equipment of an aircraft to prevent special cases in flight

Abstract

Prediction of special cases in flight is the main task of parametric diagnostics of aircraft technological equipment. To solve this problem, on-board means of automated control, diagnostics and control of on-board equipment, unloading and information support of the crew use mathematical models based on the trend analysis of some registered operating time parameters. However, existing diagnostic models based on the corresponding mathematical models do not always allow predicting the occurrence of process equipment failures. What makes the task of forecasting special cases in flight relevant. The paper proposes a method for predicting special cases in flight based on identifying abnormal sequences in the diagnostic data of aircraft technological equipment. To identify abnormal sequences, it is proposed to use a hybrid stochastic model based on a combination of Markov and production models using temporal rules to specify transition probabilities between process states. By including more precise production rules in the model, the probability of describing random processes that are not Markovian is increased, and it becomes possible to integrate a priori expert knowledge into the model, which is very important for predicting special cases in flight. The application of the proposed method will allow to implement the prognostic principle of flight safety management, as well as to obtain the economic effect of preventing aircraft downtime due to sudden equipment failure.