IWOA-Optimized Deep Learning for Bearing Fault Diagnosis Under Noisy and Variable Conditions

Bearing fault diagnosis holds immense significance for maintaining the stable operation of bearings. However, existing approaches encounter formidable challenges regarding accuracy and efficiency. This article proposes an innovative approach that leverages a deep learning model optimized by the improved whale optimization algorithm (IWOA), enabling accurate and efficient bearing fault diagnosis. In terms of diagnosis model construction, this article integrates the multiscale convolution neural network (MSCNN), bidirectional Mogrifier-gated recurrent unit (BiMGRU), and multihead attention mechanism (MHAM) to develop the MSCNN-BiMGRU-MHAM model. MSCNN serves to extract multiscale spatial features, enhancing the model’s ability to learn the characteristics of diverse data and improving the model’s generalization under variable working conditions. BiMGRU captures the temporal correlation features, effectively enhancing the context information interaction. MHAM performs parallel calculations, achieving parallel processing of fault-related information and improving the diagnosis efficiency. What is more, for model training, an IWOA, integrating a chaotic strategy, a convergence factor nonlinear strategy, and a weight adaptive strategy, is proposed. It is utilized to optimize the model’s crucial hyperparameters synchronously, achieving the optimal structure. This approach overcomes the drawbacks of relying on manual experience for parameters adjustment in traditional models, which significantly improves the efficiency and accuracy of model training. Consequently, significantly improving the efficiency and accuracy of model training. The effectiveness of the proposed approach is validated through bearing fault diagnosis experiments under noisy and diverse working conditions. Both public and real datasets are employed in these experiments. The results demonstrate that the proposed approach outperforms existing methods, underscoring its substantial practical application value.