A fast and effective fault diagnosis system is crucial for ensuring complex mechanical equipment’s safe and reliable operation. Deep learning has shown promising prospects in fault diagnosis applications, but existing algorithms have limitations in frequency analysis and long-time sequence feature encoding, which greatly restricts the practical application of deep models in the diagnosis field. This article proposes a transformer framework based on fast Fourier transform (FFT), called FFT-Trans, for mechanical fault diagnosis to overcome these limitations. FFT-Trans creatively extends the global information interaction mechanism of the transformer from the time domain to the frequency domain, thereby realizing global correlation encoding in the frequency domain and mining hidden fault features. Specifically, we replace the self-attention layer in the transformer with the global frequency encoding layer (GFE-Layer) and use learnable filters for global information exchange and better multiscale fusion. This approach can transform different types of signals into frequency components for analysis. By analyzing different frequency components in the frequency domain, the fault type and location appearing in the signal can be more accurately determined. In addition, it can fully extract the inherent connection between the vibration signal and the fault, achieving more comprehensive fault detection. We conducted experiments on the high-speed aviation bearings dataset and motor bearing dataset to validate the proposed method. The experimental results show that FFT-Trans has a better performance compared to existing deep diagnostic models, and still has considerable fault diagnosis performance in noisy environments.

中文翻译：

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FFT-Trans：利用基于傅里叶变换的变压器增强噪声条件下机械故障诊断的鲁棒性

快速有效的故障诊断系统对于保证复杂机械设备的安全可靠运行至关重要。深度学习在故障诊断应用中展现出了广阔的前景，但现有算法在频率分析和长时间序列特征编码方面存在局限性，极大地限制了深度模型在诊断领域的实际应用。本文提出了一种基于快速傅立叶变换 (FFT) 的变压器框架（称为 FFT-Trans），用于机械故障诊断，以克服这些限制。 FFT-Trans创造性地将变压器的全局信息交互机制从时域延伸到频域，从而实现频域的全局相关编码，挖掘隐藏的故障特征。具体来说，我们用全局频率编码层（GFE-Layer）替换变压器中的自注意力层，并使用可学习的滤波器进行全局信息交换和更好的多尺度融合。这种方法可以将不同类型的信号转换成频率分量进行分析。通过分析频域中的不同频率分量，可以更准确地确定信号中出现的故障类型和位置。此外，还可以充分提取振动信号与故障之间的内在联系，实现更全面的故障检测。我们在高速航空轴承数据集和电机轴承数据集上进行了实验来验证所提出的方法。实验结果表明，FFT-Trans相比现有的深度诊断模型具有更好的性能，并且在噪声环境下仍然具有可观的故障诊断性能。