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.

中文翻译：

---

基于注意力的自适应多模态特征融合网络对变工况下仓储堆垛机械的智能故障诊断

由于仓储堆垛机械工作环境恶劣，重要部件的故障信息显着复杂，导致现有基于深度学习的故障诊断方法存在分类精度低、计算复杂度高等问题。为了缓解这一问题，本文提出了一种基于注意力的自适应多模态特征融合网络的存储堆垛机械智能故障诊断架构，旨在提高特征融合网络的诊断精度和鲁棒性，并学习更广泛的特征表示。首先引入长短期记忆层提取多个时间步的特征信息，提高多时间特征的自提取能力。然后，利用最大时间特征融合模块来突出深度融合特征的可识别性。最后，添加具有跨越连接的残差层，以提高深度融合特征的利用率和表征能力。实验结果表明，与一些最先进的基于深度学习的方法相比，该方法在可变工况下的仓储堆垛机械智能故障诊断方面具有有效性和优越性。