An improved CS - Transformer for fault diagnosis of rotating machinery bearings under strong noise conditions
Abstract
针对利用现有深度学习模型对传感器采集的故障振动信号进行分类和诊断时缺乏机理分析的问题，本文提出了一种基于Transformer的故障诊断模型，即CS-Transformer。该模型通过宽卷积核和平方运算增强了故障振动信号的局部特征表示，通过利用全局平均池化提高了全局特征的鲁棒性，并采用单层 Transformer 编码器来揭示全局特征之间的相关性，从而进一步关注关键故障特征。基于 CWRU 和 Paderborn 轴承数据集进行了故障诊断实验。当信噪比为 -6 dB 时，该模型的抗噪性超过 91%，明显优于其他同类模型。这验证了该模型在强噪声条件下对不同程度的轴承故障的卓越分类性能和泛化能力。此外，对可视化包络频谱的分析进一步证实了该模型能够有效增强目标故障频率并抑制噪声。
Downloads
Published
2025-07-08
Issue
Section
Original Article
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY] that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).