College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China.
College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China.
Comput Intell Neurosci. 2022 Mar 30;2022:1875011. doi: 10.1155/2022/1875011. eCollection 2022.
The health status of mechanical bearings concerns the safety of equipment usage. Therefore, it is of crucial importance to monitor mechanical bearings. Currently, deep learning is the mainstream approach for this task. However, in practical situations, the majority of fault samples have the issue of severe class unbalancing, which renders conventional deep learning inapplicable. Targeted at this issue, this paper proposes an invariant temporal-spatial attention fusion network called ITSA-FN for bearing fault diagnosis under unbalanced conditions. First, the proposed method utilizes the invariant temporal-spatial attention representation section, which consists of a pretrained convolutional auto-encoder model, a convolutional block attention module, and a long short-term memory network, to extract independent features and invariant features of spatial-temporal characteristics from input signals. Then, a multilayer perceptron is used to fuse and infer from the extracted features and design a new loss function from the focal loss for network training. Finally, this article validates proposed model's effectiveness through comparative experiments, ablation studies, and generalization performance experiments.
机械轴承的健康状况关乎设备使用的安全性，因此对其进行监测至关重要。目前，深度学习是这项任务的主流方法。然而，在实际情况下，大多数故障样本都存在严重的类别不平衡问题，这使得传统的深度学习方法无法适用。针对这一问题，本文提出了一种名为 ITSA-FN 的不变时频注意力融合网络，用于不平衡条件下的轴承故障诊断。首先，所提出的方法利用不变时频注意力表示部分，该部分由预训练的卷积自动编码器模型、卷积块注意力模块和长短时记忆网络组成，从输入信号中提取独立特征和时频特征的不变特征。然后，使用多层感知机对提取的特征进行融合和推断，并从焦点损失中为网络训练设计新的损失函数。最后，通过对比实验、消融研究和泛化性能实验验证了所提出模型的有效性。
