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 paper 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 paper integrates the multi-scale Convolution Neural Network (MSCNN), Bidirectional Mogrifier-Gated Recurrent Unit (BiMGRU), and Multi-head Attention Mechanism (MHAM) to develop the MSCNN-BiMGRU-MHAM model. MSCNN serves to extract multi-scale spatial features, enhancing the model's ability to learn the characteristics of diverse data and improving the model 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’s 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 hyper-parameters synchronously, achieving to the optimal structure. This approach overcomes the drawbacks of relying on manual experience for parameter adjustment in traditional models. 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.