Transactions of Nanjing University of Aeronautics & Astronautics
An Effective Fault Diagnosis Method for Aero Engines Based on GSA-SAE
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Abstract:
The health status of aero engines is very important to the flight safety. However， it is difficult for aero engines to make an effective fault diagnosis due to its complex structure and poor working environment. Therefore， an effective fault diagnosis method for aero engines based on the gravitational search algorithm and the stack autoencoder （GSA-SAE） is proposed， and the fault diagnosis technology of a turbofan engine is studied. Firstly， the data of 17 parameters， including total inlet air temperature， high-pressure rotor speed， low-pressure rotor speed， turbine pressure ratio， total inlet air temperature of high-pressure compressor and outlet air pressure of high-pressure compressor and so on， are preprocessed， and the fault diagnosis model architecture of SAE is constructed. In order to solve the problem that the best diagnosis effect cannot be obtained due to manually setting the number of neurons in each hidden layer of SAE network， a GSA optimization algorithm for the SAE network is proposed to find and obtain the optimal number of neurons in each hidden layer of SAE network. Furthermore， an optimal fault diagnosis model based on GSA-SAE is established for aero engines. Finally， the effectiveness of the optimal GSA-SAE fault diagnosis model is demonstrated using the practical data of aero engines. The results illustrate that the proposed fault diagnosis method effectively solves the problem of the poor fault diagnosis result because of manually setting the number of neurons in each hidden layer of SAE network， and has good fault diagnosis efficiency. The fault diagnosis accuracy of the GSA-SAE model reaches 98.222%， which is significantly higher than that of SAE， the general regression neural network （GRNN） and the back propagation （BP） network fault diagnosis models.
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TH136
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