Xiao Zhiguo, Li Dongni, Yang Chunguang, Chen Wei
School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100811, China.
College of Computer Science and Technology, Changchun University, Changchun 130022, China.
Sensors (Basel). 2024 Aug 10;24(16):5181. doi: 10.3390/s24165181.
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.
为解决滚动轴承特征提取不足、故障诊断不准确以及在复杂运行条件下过拟合等问题，本文提出一种基于多尺度特征融合与迁移对抗学习的滚动轴承诊断方法。首先，设计了一个多尺度卷积融合层，以在多个时间尺度上有效地从原始振动信号中提取故障特征。通过基于多头注意力机制的特征编码融合模块进行特征融合提取，该模块可以对长距离上下文信息进行建模，并显著提高诊断精度和抗噪声能力。其次，基于迁移学习方法的域自适应（DA）跨域特征对抗学习策略，通过缩小目标域与源域之间的数据分布差距，实现最优域不变特征的提取，满足了跨运行条件、设备以及虚拟现实迁移的故障诊断研究需求。最后，进行实验以验证和优化特征提取与融合网络的有效性。使用一个公共轴承数据集作为源域数据，并选择特种车辆轴承数据作为目标域数据，对网络迁移学习效果进行对比实验。实验结果表明，所提出的方法在跨域和变负载环境中表现出卓越的性能。在多个轴承跨域迁移学习任务中，该方法实现了高达98.65%的平均迁移故障诊断准确率。与现有方法相比，所提出的方法显著增强了数据特征提取能力，从而实现了更稳健的诊断性能。
