Abstract: A method for prediction of key performance parameters of an aero-engine transition state acceleration process based on space reconstruction. Aero-engine transition state acceleration process test data provided by a research institute is used for establishing a training dataset and a testing dataset; dimension increase is conducted on the datasets based on the data space reconstruction of an auto-encoder; model parameters optimization is conducted by population optimization algorithms which is represented by particle swarm algorithm; and random forest regression algorithm performing well on high-dimensional data is used for carrying out regression on transition state performance parameters, which realizes effective real-time prediction from the perspective of engineering application.

Abstract: The present invention belongs to the technical field of fault diagnosis of aero-engines, and provides a method for fault diagnosis of an aero-engine rolling bearing based on random forest of power spectrum entropy. Aiming at the above-mentioned defects existing in the prior art, a method for fault diagnosis of an aero-engine rolling bearing based on random forest is provided, wherein test measured data for an aero-engine rolling bearing provided by a research institute are used for establishing a training dataset and a test dataset first; and based on an idea of fault feature extraction, time domain statistical analysis and frequency domain analysis are conducted on original collection data by adopting wavelet analysis; thereby realizing effective fault diagnosis from the perspective of engineering application.

Abstract: A method for prediction of key performance parameters of an aero-engine in transition condition. Bench test data for an aero-engine in transition condition provided by a research institute is used for establishing a training dataset and a testing dataset first; parameter combination is used for predicting and analyzing engine exhaust temperature based on the idea of information fusion; and the method of rolling windows is used for rolling learning in order to predict the parameters such as low pressure rotor speed and exhaust temperature of an engine from the perspective of practical engineering application.

Abstract: A prediction method for an aero-engine starting exhaust temperature. A prediction model for the engine starting exhaust temperature is obtained by using a machine learning-based method and aero-engine ground test data. The model has high prediction accuracy and good generalization ability. The prediction result can be further used for engine control, etc., reducing the possibility of overheating of the engine. Compared with the traditional single parameter prediction, this contains more information because of using fusion prediction, so that prediction errors are reduced; and compared with the single prediction algorithm, this assembles weak learner by means of an AdaBoost. RT ensemble algorithm, so that the prediction errors are smaller.

Abstract: The present invention belongs to the technical field of fault diagnosis of aero-engines, and provides a method for fault diagnosis of an aero-engine rolling bearing based on random forest of power spectrum entropy. Aiming at the above-mentioned defects existing in the prior art, a method for fault diagnosis of an aero-engine rolling bearing based on random forest is provided, wherein test measured data for an aero-engine rolling bearing provided by a research institute are used for establishing a training dataset and a test dataset first; and based on an idea of fault feature extraction, time domain statistical analysis and frequency domain analysis are conducted on original collection data by adopting wavelet analysis; thereby realizing effective fault diagnosis from the perspective of engineering application.

Abstract: A method for prediction of key performance parameters of an aero-engine transition state acceleration process based on space reconstruction. Aero-engine transition state acceleration process test data provided by a research institute is used for establishing a training dataset and a testing dataset; dimension increase is conducted on the datasets based on the data space reconstruction of an auto-encoder; model parameters optimization is conducted by population optimization algorithms which is represented by particle swarm algorithm; and random forest regression algorithm performing well on high-dimensional data is used for carrying out regression on transition state performance parameters, which realizes effective real-time prediction from the perspective of engineering application.

Abstract: A method for prediction of key performance parameters of an aero-engine in transition condition. Bench test data for an aero-engine in transition condition provided by a research institute is used for establishing a training dataset and a testing dataset first; parameter combination is used for predicting and analyzing engine exhaust temperature based on the idea of information fusion; and the method of rolling windows is used for rolling learning in order to predict the parameters such as low pressure rotor speed and exhaust temperature of an engine from the perspective of practical engineering application.