A Hydraulic Turbine Fault Diagnosis Method Based on Synchrosqueezed Wavelet Transform and SE-ResNet

To tackle the challenges associated with conventional methods of diagnosing hydraulic turbine faults, which depend heavily on expert knowledge and exhibit low efficiency and precision, a model for detecting hydraulic turbine faults has been developed that integrates the synchrosqueezed wavelet transform (SWT) with SE-ResNet. Initially, a 1D non-stationary vibration signal is converted into a high-frequency time–frequency representation in two dimensions using SWT, which then acts as the input for the convolutional neural network. Secondly, a model based on SE-ResNet is designed, incorporating an attention mechanism that enhances the extraction of features from two-dimensional images, thereby increasing the emphasis on crucial features and bolstering the model’s representation capabilities. Finally, results related to fault detection are produced via the softmax layer. To evaluate the proposed model’s efficiency, two datasets were utilized for the experiments conducted, one sourced from Case Western Reserve University and the other from hydraulic turbine vibration signals. Compared to conventional approaches, this technique demonstrates significant practicality and effective convergence characteristics, offering considerable value in real-world applications.