Method for monitoring exhaust temperature of aircraft engine

Abstract

The invention discloses a method for monitoring exhaust temperature of an aircraft engine. The method comprises following steps: (1) acquiring and storing data of exhaust temperature of the engine; (2) extracting historical data of the exhaust temperature of the engine in an appointed time interval, and building a multi-dimensional training sample set and a testing sample set which are used for building and testing prediction models and building model input for prediction; (3) building a prediction model on the basis of a neural network in a convolution sum discrete process, utilizing a Levenberg-Marquardt algorithm to train the prediction model and testing and storing the prediction model; and (4) utilizing the prediction model to predict the exhaust temperature of the engine and applying the prediction model to monitoring states of the aircraft engine. The method is favorable for increasing the state monitoring and repair guarantee level of the aircraft engine, and accordingly achieves the purpose of guaranteeing air safety.

Description

technical field [0001] The invention relates to a method for monitoring the exhaust temperature of an aeroengine. Background technique [0002] Aeroengine is the heart of an aircraft, and its health is a key factor affecting aviation safety. Exhaust temperature is a key parameter that characterizes the health status of an aeroengine and determines whether the engine can be used safely. The safe operation of the engine, and provide decision support for the maintenance of the engine. Since the exhaust temperature is affected by many complex nonlinear time-varying factors in the process of changing with time, it is a practical technical problem to accurately predict its change trend. In the prior art, only the process neural network can predict the exhaust temperature. The time accumulation effect of time is effectively processed, and when the process neural network is used to predict the exhaust temperature, the collected discrete exhaust temperature data must first be fitte...

AI Technical Summary

Problems solved by technology

The shortcomings of this processing method are: (1) the discrete sampling data may not exist in the form of analytical functions; (2) when performing orthogonal basis expansion on the input function and weight function, there is no theoretical basis for the number of basis functions to be selected, and only Determined by trial and error; (3) The process of fitting discrete samples to generate input functions, and performing basis expansion on input functions and weight functions may cause certain information loss and lead to loss of accuracy

The above deficiencies make the use of the existing process neural network model complex, and it is difficult to achieve good results in the prediction of engine exhaust temperature, thus limiting the application of process neural network in the actual engineering of aeroengine exhaust temperature monitoring

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