Bearing fault diagnosis method based on WSST and ISSA-MCNN-BIGRU

Rolling bearings constitute essential components in large-scale rotating machinery, nonetheless, their fault diagnosis still encounters significant challenges, including the difficulty of extracting discriminative features, relatively low recognition rates, and heavy reliance on expert experience. To address these challenges, this paper proposes a hybrid diagnostic framework that integrates the Wavelet Synchrosqueezed Transform (WSST), an Improved Sparrow Search Algorithm (ISSA), a Multi-Scale Convolutional Neural Network (MCNN), and a Bidirectional Gated Recurrent Unit (BiGRU). First, WSST is employed to obtain high-resolution time-frequency representations that capture subtle transient characteristics of bearing vibration signals. Second, MCNN performs multi-scale spatial feature extraction on the WSST-generated images, enabling the simultaneous capture of fine-grained and coarse-grained fault patterns. Third, BiGRU is introduced to learn bidirectional temporal dependencies, thereby enhancing the model's capability to represent sequential data. Crucially, ISSA-augmented with chaotic Tent mapping, a Gaussian mutation strategy, and a Levy flight mechanism-is applied to adaptively optimize key hyperparameters of the MCNN-BiGRU network (learning rate, convolutional kernel sizes, number of GRU units). Experimental results on both the Case Western Reserve University and Southeast University bearing datasets demonstrate that the proposed ISSA-MCNN-BiGRU model achieves a fault diagnosis accuracy of up to 99.75%, outperforming baseline models such as standalone GRU, BiGRU, MCNN-BiGRU, PSO-MCNN-BiGRU, and GA-MCNN-BiGRU in terms of accuracy, stability, and generalization. Additionally, in different noise environments, the proposed model's accuracy is significantly higher than that of comparative models, demonstrating strong robustness.