SCBM-Net:一种基于聚变的多模式功能双通道方法,用于轴承故障诊断
Qiang Liu1, Weiyan Tong2, Hongwei Bai1
1School of Chemical Process Automation, Shenyang University of Technology, Liaoyang, 111003, China.
Scientific reports
|October 30, 2025
概括
新型深度学习模型SCBM-Net通过使用双通道多式联接来增强轴承故障诊断. 它即使在有限的数据和噪声下也能达到高准确度,并显示出强大的概括性和可转移性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 单模方法在复杂的轴承故障诊断方面存在困难.
- 需要强大的模型来处理不同的操作条件和有限的数据.
研究的目的:
- 提出SCBM-Net,一个双通道多式联络深度学习模型,用于高级轴承故障诊断.
- 通过创新的数据融合,提高特征表示和模型稳定性.
主要方法:
- 使用连续波形变换 (CWT) 和变化模式分解 (VMD) 来进行特征提取.
- 使用Swin变压器进行CWT图像分析,使用CNN-BiGRU-Attention网络进行VMD信号分析.
- 集成功能使用多模式紧双线聚合 (MCB) 模块.
主要成果:
- 在干净条件下,在CWRU数据集上实现了99.83%的准确性.
- 在几次学习设置 (60个样本/班级) 中保持了98.64%的准确性.
- 在不平衡的数据集上表现出强的表现 (97.33%年均). 准确度) 和跨领域的概括 (98.33%在SEU数据集).
- 在严重噪音下 (-10 dB) 保持了80.67%的准确性.
结论:
- 与现有方法相比,SCBM-Net在轴承故障诊断方面提供了卓越的性能.
- 该模型具有出色的概括性,可转移性和噪声强度.
- 双通道多式联络融合有效地提高了故障诊断能力.
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