Simulation data driven time–frequency fusion 1D convolutional neural network with multiscale attention for bearing fault diagnosis

Abstract This paper presents a bearing fault diagnosis model based on a simulated data-driven time-domain and frequency-domain feature fusion using a one-dimensional convolutional neural network (1D CNN). The proposed simulation data-driven time–frequency feature fusion 1D CNN multiscale attention (TFF-MSA) model solves the problem of insufficient generalization ability due to the scarcity of experimental data through the simulation data of the dynamic model of bearings, and accurately classifies the bearing faults in a strong noise environment. Traditional 1D CNN-based bearing fault diagnosis models often focused on either time-domain signals or frequency-domain spectra. The proposed model incorporates a parallel 1D CNN structure that simultaneously extracts features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information from the bearing vibration signals. Specifically, to enhance the extraction of time-domain features, we introduce the pyramid attention module to mine multiscale features. Meanwhile, in the frequency-domain feature extraction, a specific fault feature frequency weighting method, allowing the proposed model to extract specific fault-related frequencies from the signals, is also proposed. Experimental results demonstrate that the proposed model which is only driven by simulated signals from dynamic model achieves an average diagnostic accuracy of 94%.