A High-Precision Aero-Engine Bearing Fault Diagnosis Based on Spatial Enhancement Convolution and Vision Transformer

In aeronautical engineering, monitoring and diagnosing inter-shaft bearings of aero-engine is crucial for flight and life safety. Given that sensor signals are affected by strong noise environments, this study proposes a high-precision multi-sensor feature fusion fault diagnosis method. The method is based on the lightweight spatial enhancement convolutional module with channel shuffling and combined with vision transformer (CSST-Net). Firstly, the original one-dimensional time-series signals acquired from multiple sensors are converted into two-dimensional time-frequency images using wavelet transform. The data is then fed into the model by utilizing data layer feature fusion. Subsequently, the interaction of information can be better facilitated by introducing channel shuffling operation into the convolution module. Additionally, the spatial enhancement algorithm leverages human visual perception properties to extract deeper fault features from the samples. Finally, the global information is extracted by the vision transformer to obtain the fault diagnosis results. By validating on aero-engine inter-shaft bearing and rolling bearing datasets and comparing with other classical and advanced fault diagnosis methods. The results show that the method proposed in this study is not only state-of-the-art but also extremely robust in strong noise environments. Code is available at https://github.com/DABINB/CSST-Net.