Deep learning has been widely used for intelligent fault diagnosis of rotating machinery. However, owing to the limitations of training sample data and the complex industrial environments with variable operating conditions and noise interference, the existing deep learning-based fault diagnosis methods have difficulty achieving satisfactory performance. To address these issues, this paper proposes a novel multi-sensor local and global feature fusion architecture for intelligent fault diagnosis. First, through the integration of a stem structure and one-dimensional convolutions, a local feature perception mechanism is constructed to learn the sensor-specific local features within each sensor. Second, a global feature perception mechanism, which incorporates a multi-sensor sparse Transformer and a hierarchical architecture, is established to fully explore the sensor-specific global features within each sensor and the cross-sensor global features among multiple sensors. Third, the sensor-specific and cross-sensor global features are fed into a feature aggregation module to obtain the final multi-sensor global features. Finally, these multi-sensor global features are classified through a multi-sensor feature classifier to obtain diagnostic results. The experimental results obtained for a gear case and an inter-shaft bearing case demonstrate the superior diagnostic performance of the proposed method compared to the state-of-the-art comparative methods under limited training samples and complex environments.

中文翻译：

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一种基于多传感器稀疏 Transformer 的新型多传感器局部和全局特征融合架构，用于智能故障诊断


深度学习已广泛应用于旋转机械的智能故障诊断。然而，由于训练样本数据的限制，以及工作条件多变、噪声干扰的复杂工业环境，现有的基于深度学习的故障诊断方法难以取得令人满意的性能。针对这些问题，该文提出了一种新颖的多传感器局部和全局特征融合架构，用于智能故障诊断。首先，通过词干结构和一维卷积的集成，构建了局部特征感知机制，以学习每个传感器内部特定于传感器的局部特征。其次，建立了融合多传感器稀疏 Transformer 和分层架构的全局特征感知机制，充分挖掘每个传感器内部特定于传感器的全局特征以及多个传感器之间的跨传感器全局特征。第三，将特定于传感器的全局特征和跨传感器的全局特征馈送到特征聚合模块中，以获得最终的多传感器全局特征。最后，通过多传感器特征分类器对这些多传感器全局特征进行分类，得到诊断结果。在齿轮箱和轴间轴承箱中获得的实验结果表明，在有限的训练样本和复杂环境下，与最先进的比较方法相比，所提方法具有优异的诊断性能。