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Aeroengine Fault Prediction Based on Bidirectional LSTM Neural Network

As the power system of an airplane, the safety and reliability of an aero-engine are related to the normal operation of the whole airplane, and more importantly to the safety of people's lives and property. Based on the idea of bi-directional LSTM neural network modeling and information fusion, this paper establishes a multi-fault characteristic parameter prediction model by combining the relationship between the sampled data of several characteristic parameters of an aircraft engine and the actual characteristics of the prediction sequence. The wavelet packet decomposition technology is applied to extract the energy values of each frequency band of the rotor vibration signal as the input values of the bidirectional LSTM neural network system, to predict the energy changes of each frequency band, to reconstruct the feature vector composed of the predicted values, and to apply to the fault prediction analysis of the aero-engine rotor. The test of measured data shows its better prediction effect.