Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.