Aero-engine Bearing Fault Diagnosis Based on Deep Neural Networks

For aero-engines, bearing fault monitoring and diagnosis can greatly improve its reliability. Based on the successful application of deep learning algorithms in the mapping of complex nonlinear systems, this paper proposes an aero-engine bearing fault diagnosis method that combines convolutional neural networks and support vector machine. Firstly, the Short-time Fourier transform is used to perform time-frequency analysis on time series signals (including nine faults and one normal) to obtain corresponding time-frequency images. Then, the convolutional neural network is used to extract the feature of the obtained time-frequency images. Furthermore, the support vector machine is used to classify and diagnose the faults from the extracted feature. Finally, based on the data set of Case Western Reserve University, the feasibility is validated. The results show that the proposed method has achieved a bearing fault recognition rate of more than 99%, which can effectively complete the fault diagnosis task.