Multimodal-based model for asynchronous motor fault diagnosis under noisy and variable operating conditions: a novel hybrid approach

Motors are pivotal in modern industry, especially as global demand for automation and smart manufacturing surges. Accurate fault diagnosis is crucial for stability maintenance, but existing approaches lack satisfactory accuracy and efficiency. This study integrates the multi-scale Convolution Neural Network (MSCNN),Bidirectional Mogrifier-Gated Recurrent Unit (BiMGRU), and Multi-head Attention Mechanism (MHAM) to propose a multimodal-based hybrid model of MSCNN-BiMGRU + MHAM for asynchronous motor fault diagnosis. The MSCNN channel is responsible for spatial feature extraction, and the BiMGRU channel is responsible for temporal feature extraction. While MHAM is responsible for efficient integration and extraction of multimodal features. Furthermore, to refine the model's performance, an enhanced whale optimization algorithm (EWOA) is innovatively presented and embedded during model training, systematically optimizing hyperparameters to boost model generalization and training effectiveness. Numerous validations are conducted by the real vibration datasets of asynchronous motor gathered under noisy and various operating conditions. Compared to traditional approaches and the current mainstream deep learning models, the proposed hybrid model with EWOA optimization attains the impressive prediction accuracy. It delivers an effective and efficient approach to tackle the issues of motor fault diagnosis.