Advanced deep-learning models have shown excellent performance in the task of fault-bearing diagnosis over traditional machine learning and signal-processing techniques. Few-shot learning approach has also been attracting a lot of attention in this task to address the problem of limited training data. Nevertheless, cutting-edge models for fault-bearing diagnosis are often based on convolutional neural networks (CNNs) that emphasize local features of input data. Besides, accurate classification of fault-bearing signals is still nontrivial due to the variations of data, fault types, acquisition conditions, and extremely limited data, leaving space for research on this topic. In this study, we propose a novel end-to-end approach for fault-bearing diagnosis even in the case of limited data with artificial and real faults. In particular, we propose a module for automatic feature extraction from input data namely multiscale large kernel feature extraction. The extracted features are then fed into a two-branch model including a global and a local branch. The global one includes a transformer architecture with cross-attention to handle global context and obtain the correlation between the query and support sets. The local branch is a metric-based model consisting of Mahalanobis distance for separating local features from the support set. The outputs from the two branches are then ensembled for classification purposes. Intensive experiments and ablation studies have been made on the two public datasets including CWRU and PU. Qualitative and quantitative results with different degrees of training samples by the proposed model in comparison with other state-of-the-arts have shown the superior performance of the proposed approach. Our code will be published at https://github.com/HungVu307/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance

中文翻译：

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通过基于集成变压器的模型以及从多尺度特征进行马哈拉诺比斯距离度量学习进行少样本轴承故障诊断

与传统的机器学习和信号处理技术相比，先进的深度学习模型在故障诊断任务中表现出了优异的性能。在这项任务中，为了解决训练数据有限的问题，少样本学习方法也引起了很多关注。然而，用于故障诊断的前沿模型通常基于强调输入数据局部特征的卷积神经网络（CNN）。此外，由于数据、故障类型、采集条件的变化以及数据的极其有限，对故障信号的准确分类仍然很重要，这给该课题的研究留下了空间。在这项研究中，我们提出了一种新颖的端到端故障诊断方法，即使在人工和真实故障数据有限的情况下也是如此。特别是，我们提出了一个从输入数据自动提取特征的模块，即多尺度大核特征提取。然后将提取的特征输入到包括全局分支和局部分支的双分支模型中。全局架构包括具有交叉注意力的转换器架构，用于处理全局上下文并获取查询和支持集之间的相关性。局部分支是一个基于度量的模型，由马哈拉诺比斯距离组成，用于将局部特征与支持集分开。然后将两个分支的输出进行集成以用于分类目的。在 CWRU 和 PU 两个公共数据集上进行了深入的实验和消融研究。与其他最先进的技术相比，所提出的模型在不同程度的训练样本上的定性和定量结果表明了所提出方法的优越性能。我们的代码将发布在https://github.com/HungVu307/Few-shot-via-ensembling-Transformer-with-Mahalanobis-distance