Augmentation-based discriminative meta-learning for cross-machine few-shot fault diagnosis
Augmentation-based discriminative meta-learning for cross-machine few-shot fault diagnosis
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DOI：
10.1007/s11431-022-2380-0
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发表时间：
2023-05
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影响因子：
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通讯作者：
Pengcheng Xia;Yixiang Huang;Yuxiang Wang;Chengliang Liu;Jie Liu
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文献类型：
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作者：
Pengcheng Xia;Yixiang Huang;Yuxiang Wang;Chengliang Liu;Jie Liu
深度学习方法在故障诊断任务中表现出了良好的性能。虽然工业场景中数据的稀缺限制了这些方法的实际应用，但迁移学习通过跨机器知识迁移有效地解决了这个问题。然而，跨机器的几杆问题，这是一个更一般的工业场景，很少被调查。现有的研究没有考虑跨机域迁移问题，导致测试性能不佳。本文提出了一种基于增广的判别元学习方法来解决这个问题。在元训练过程中，提出了信号变换以增加元任务的多样性，以实现更鲁棒的特征学习，并结合多尺度学习以实现更自适应的特征嵌入。在元测试过程中，有限的标记故障信息被用来促进模型的泛化在目标领域通过准元训练的数据增强。此外，提出了一种新的双曲型原型损失，通过设计一个双曲型决策边界，更有区别的特征表示和可分离的类别原型。跨机器少拍诊断实验进行了使用三个数据集从不同的机器，即，轴承，电机和齿轮数据集。通过烧蚀和对比研究验证了该方法的有效性。
Deep learning methods have demonstrated promising performance in fault diagnosis tasks. Although the scarcity of data in industrial scenarios limits the practical application of such methods, transfer learning effectively tackles this issue through cross-machine knowledge transfer. Nevertheless, the cross-machine few-shot problem, which is a more general industrial scenario, has been rarely investigated. Existing studies have not considered the cross-machine domain shift problem, which results in poor testing performance. This paper proposes an augmentation-based discriminative meta-learning method to address this issue. In the meta-training process, signal transformation is proposed to increase the meta-task diversity for more robust feature learning, and multi-scale learning is combined for more adaptive feature embedding. In the meta-testing process, limited labeled fault information is used to promote model generalization in the target domain through quasi-meta-training based on data augmentation. Furthermore, a novel hyperbolic prototypical loss is proposed for more discriminative feature representation and separable category prototypes by designing a hyperbolic decision boundary. Cross-machine few-shot diagnosis experiments were conducted using three datasets from different machines, namely, the bearing, motor, and gear datasets. The effectiveness of the proposed method was verified through ablation and comparison studies.