Engineering fault diagnosis often needs to be implemented without prior knowledge of labels. Considering the randomness and drift of fault features, this paper proposes fusing multichannel autoencoders with dynamic global loss (FMA-DGL) to enhanc self-supervised fault diagnosis. Multiple autoencoders are employed to represent the fault features of multichannel vibration signals. A dynamic global loss function is utilized to self-supervise the generation of pseudo-labels, thereby integrating multichannel feature information together. The proposed dynamic global loss controls the degree of conflict of samples from different channels to construct clustering centers, allowing the clustering process to converge more smoothly. By leveraging both the common and complementary information across different channels, the randomness and drift issues of self-supervised pseudo-labels are addressed, effectively enhancing the fault diagnosis performance through multichannel fusion. Experiments were carried out using a public bearing dataset and a rotating machinery experimental setup, respectively. Results show that the proposed FMA-DGL outperforms the state-of-the-art peer methods, exhibiting good results and applicability in self-supervised fault diagnosis based on multichannel vibration signals.

中文翻译：

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将多通道自动编码器与动态全局损失融合以进行自监督故障诊断


工程故障诊断通常需要在不事先了解标签的情况下实施。考虑到故障特征的随机性和漂移性，本文提出融合多通道自动编码器与动态全局损失（FMA-DGL）来增强自监督故障诊断。采用多个自编码器来表示多通道振动信号的故障特征。利用动态全局损失函数来自我监督伪标签的生成，从而将多通道特征信息集成在一起。所提出的动态全局损失控制来自不同通道的样本构建聚类中心的冲突程度，使聚类过程更加平滑地收敛。通过利用不同通道之间的公共和互补信息，解决了自监督伪标签的随机性和漂移问题，通过多通道融合有效提高了故障诊断性能。分别使用公共轴承数据集和旋转机械实验装置进行了实验。结果表明，所提出的 FMA-DGL 优于最先进的同行方法，在基于多通道振动信号的自监督故障诊断中表现出良好的结果和适用性。