Bearing fault diagnosis based on POA-VMD with GADF-Swin Transformer transfer learning network

Abstract

To address the challenges posed by early weak fault signals hindering fault feature extraction and leading to diminished recognition accuracy in deep learning models, we present a method for diagnosing bearing faults. This method integrates the Pelican Optimization Algorithm (POA), Variational Mode Decomposition (VMD), Gramian Angular Difference Fields (GADF), and the Swin Transformer network. Initially, the VMD’s key parameter are optimized through POA to dynamically yield the optimal parameter combination. Subsequently, the refined VMD is employed to decompose the original signal. The intrinsic modal function (IMF) is subsequently filtered, and the signal is reconstructed using the weighted composite kurtosis(WCK) index. Following this step, the reconstructed 1D signal is converted into a 2D image using GADF and is then input into the constructed Swin Transformer transfer learning network model for the purpose of training. In conclusion, a fault diagnosis experiment was conducted using the measured signal, which showed the successful extraction of the signal’s characteristics and octave frequencies through the method presented in this paper. Moreover, concerning fault identification, an identification accuracy exceeding 95% was achieved.