Unsupervised domain adaptation method for bearing fault diagnosis assisted by twin data under extreme sample scarcity

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Zhihui Men , Dao Gong , Kai Zhou , Yuejian Chen , Jinsong Zhou
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Abstract

In small-sample bearing fault diagnosis, synthetic signals generated by simulation or generative models are commonly used to augment datasets. However, such signals often lack realistic noise and nonlinear characteristics, resulting in a domain gap between synthetic and real data. To address this, we propose an end-to-end method based on style transfer to generate twin data that better resembles real-world signals. First, a finite element model is built to derive the relationship between contact stiffness and radial force, and dynamic simulations are conducted using RecurDyn to obtain initial signals. Then, an Adaptive Style Transfer Network (AdasTNet) is employed to transfer the “style” of real signals to the simulated ones, enhancing their similarity in both time and frequency domains. The resulting twin data serves as the source domain, while the real data—without any labels—is treated as the target domain. We perform unsupervised domain adaptation using a CNN backbone combined with domain adversarial training and Maximum Mean Discrepancy (MMD) minimization. Experimental results show that the proposed method outperforms conventional GAN-based approaches in both accuracy and stability. Moreover, the model is lightweight and efficient, making it well-suited for real-world deployment in data-scarce scenarios.
极端样本稀缺性下双数据辅助轴承故障诊断的无监督域自适应方法
在小样本轴承故障诊断中,通常使用仿真或生成模型产生的合成信号来增强数据集。然而,这些信号往往缺乏真实的噪声和非线性特征,导致合成数据与真实数据之间存在域间隙。为了解决这个问题,我们提出了一种基于风格迁移的端到端方法,以生成更类似于现实世界信号的孪生数据。首先,建立有限元模型,推导接触刚度与径向力之间的关系,并利用RecurDyn进行动力学仿真,获得初始信号;然后,采用自适应风格转移网络(AdasTNet)将真实信号的“风格”转移到模拟信号中,增强了它们在时频域的相似度。得到的孪生数据作为源域,而真实数据(没有任何标签)被视为目标域。我们使用CNN主干结合领域对抗训练和最大平均差异(MMD)最小化来执行无监督域自适应。实验结果表明,该方法在精度和稳定性方面都优于传统的基于gan的方法。此外,该模型轻量级且高效,因此非常适合在数据稀缺的场景中进行实际部署。
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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