针对滚动轴承特征提取不充分的问题,不准确的故障诊断,和过拟合在复杂的操作条件下,提出了一种基于多尺度特征融合和传递对抗学习的滚动轴承诊断方法。首先,设计多尺度卷积融合层,有效地从原始振动信号中提取多时间尺度的故障特征。通过基于多头注意机制的特征编码融合模块,进行特征融合提取,它可以对远程上下文信息进行建模,并显着提高诊断准确性和抗噪声能力。其次,基于域适应(DA)跨域特征对抗学习策略的迁移学习方法,通过减少目标域与源域之间数据分布的差距来实现最优域不变特征的提取,解决了对跨运行条件的故障诊断研究的呼吁,设备,和虚实迁移。最后,实验验证和优化了特征提取和融合网络的有效性。使用公共轴承数据集作为源域数据,和特种车辆轴承数据作为目标域数据,进行网络迁移学习效果的对比实验。实验结果表明,该方法在跨域和可变负载环境中具有出色的性能。在多个轴承跨域迁移学习任务中,该方法的平均迁移故障诊断准确率高达98.65%。与现有方法相比,该方法显著增强了数据特征提取的能力,从而实现更强大的诊断性能。
To address the issues of inadequate feature extraction for rolling bearings, inaccurate fault diagnosis, and overfitting in complex operating conditions, this paper proposes a rolling bearing diagnosis method based on multi-scale feature fusion and transfer adversarial learning. Firstly, a multi-scale convolutional fusion layer is designed to effectively extract fault features from the original vibration signals at multiple time scales. Through a feature encoding fusion module based on the multi-head attention mechanism, feature fusion extraction is performed, which can model long-distance contextual information and significantly improve diagnostic accuracy and anti-noise capability. Secondly, based on the domain adaptation (DA) cross-domain feature adversarial learning strategy of transfer learning methods, the extraction of optimal domain-invariant features is achieved by reducing the gap in data distribution between the target domain and the source domain, addressing the call for research on fault diagnosis across operating conditions, equipment, and virtual-real migrations. Finally, experiments were conducted to verify and optimize the effectiveness of the feature extraction and fusion network. A public bearing dataset was used as the source domain data, and special vehicle bearing data were selected as the target domain data for comparative experiments on the effect of network transfer learning. The experimental results demonstrate that the proposed method exhibits an exceptional performance in cross-domain and variable load environments. In multiple bearing cross-domain transfer learning tasks, the method achieves an average migration fault diagnosis accuracy rate of up to 98.65%. When compared with existing methods, the proposed method significantly enhances the ability of data feature extraction, thereby achieving a more robust diagnostic performance.