Aero-engine Performance Decline Forecast Based On Process Neural Network Ensemble

Aero-engines has been described as the heart, and its performance decline condition directly affects the flight safety of the aircraft and the cost of the airline.Aero-engines performance decline mainly reflected in the quality of their performance decline parameters showing a declining trend, and the performance decline parameters are time series parameters. Therefore, when we treatment and predict the condition of the aero-engines performance decline, we must use the method which is able to handle the time-series approach. DEGT (Delta Exhaust Gas Temperature, DEGT) is one of the important parameters of the engine performance decline. In this paper, taking DEGT as an example, the predicting technique of aero-engines performance decline is researched.Because of the timing characteristics of the aero-engines performance decline parameters, this paper avoids the mathematical modeling which tedious and practice difficulty and the method of the traditional artificial neural network forecast which does not reflect the time parameters of the cumulative effect, proposes a method of the performance decline prediction based on process neural network. Then applying respectively the feedforward process neural network, two parallel process neural network, wavelet process neural networks to the predicting of the aero-engines performance decline. In this way, compares the predicted results, analyzes factors affecting the generalization ability of the process neural network.On this basis, this paper efforts to improve the forecasting accuracy and to overcome the prediction defect of the single process neural network forecasting. First, proposes the prediction method of the performance decline based on the model of process neural network ensemble forecasting. Second, describes the concepts and basic theory of the process neural network ensemble. Third, analyzes the synthesis of the output stage of the network in several ways and compares the advantages and disadvantages of each method at the same time. Forth, analyzes the many factors which impact the generalization ability of the process neural network model.For optimizing the process neural network ensemble model, this paper also analyzes many factors affecting the generalization ability of the process neural network.A software system was developed based on the theory study above, named aero-engine health condition prediction system based on process neural network. The system is used in Air China now, and has been integrated into the "Web-based aero-engine health monitoring and maintenance data management system". The system will support to realize the independence, real-time, automation and intelligence of the aero-engine performance decline prediction.