基于飞行过程数据的航空发动机故障诊断方法研究

Aiming at the data of the aircraft engine flight process，a data-driven aircraft engine fault diag⁃ nosis structure is proposed by combining the gated recurrent unit（GRU）dynamic network and the deep neural network（DNN）. Firstly，the engine health data was extracted from the flight data，and the dynamic model of the engine in a healthy state was established through a group of GRU networks. Secondly，the residual signal was gen⁃ erated by the predicted value of the GRU dynamic models and the real measurement signal，and the residual sig⁃ nal was used as the input of the DNN network to predict the engine health parameters. Finally，the engine fault detection and identification were realized by the diagnostic decision module. The proposed fault diagnosis system was verified by using the real flight condition data set of the engine generated by simulation. The results show that compared with the direct use of sensor measurement data，the residual structure based on GRU network can great⁃ ly improve the performance of fault detection and identification，and the fault detection accuracy and fault identi⁃ fication accuracy can reach 96.51% and 95.06%. The dependence on the number of training data samples is small，and good prediction results can be obtained with few training samples.