A Few-Shot Machinery Fault Diagnosis Framework Based on Self-Supervised Signal Representation Learning
A Few-Shot Machinery Fault Diagnosis Framework Based on Self-Supervised Signal Representation Learning
复制标题
基于自监督信号表示学习的少样本机械故障诊断框架
DOI：
10.1109/tim.2024.3352689
复制
发表时间：
2024
影响因子：
5.6
通讯作者：
Huan Wang;Xindan Wang;Yizhuo Yang;Konstantinos C. Gryllias;Zhiliang Liu
中科院分区：
文献类型：
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作者：
Huan Wang;Xindan Wang;Yizhuo Yang;Konstantinos C. Gryllias;Zhiliang Liu
基于深度学习的智能故障诊断方法近年来取得了可喜的成果;然而，大多数模型的性能需要许多标记样本进行训练，这在真实的行业情况下通常是不切实际的。与此同时，大量未标记的操作数据很容易获得。有效地利用和利用未标记数据中封装的丰富信息并利用有限的标记样本构建鲁棒的深度学习模型具有重要意义;因此，我们提出了一种新的少镜头学习模型，将未标记信号表示学习思想与少镜头学习算法相结合。该模型首先采用自监督学习（SSL）从大量未标记样本中获取信号的固有特征。随后，将获得的特征转移到改进的Siamese网络，以增强其在少镜头数据集上的鲁棒性和泛化能力。该方法不仅为无标记信号特征学习提供了一种新的解决方案，而且进一步推动了少样本学习方法成为一种更鲁棒和实用的技术。在两个故障诊断数据集上对该方法进行了验证，实验结果表明，该模型在极其有限的标记训练样本下具有良好的性能。
The intelligent fault diagnosis method based on deep learning has achieved promising results in recent years; however, the performance of most models requires many labeled samples for training, which is usually impractical in real industry situations. At the same time, large amounts of unlabeled operational data are easily available. It is of great significance to efficiently harness and leverage the wealth of information encapsulated within unlabeled data and build a robust deep learning model with limited labeled samples; thus, we propose a novel few-shot learning model that combines the unlabeled signal representation learning idea with the few-shot learning algorithm. The proposed model first employs self-supervised learning (SSL) to obtain inherent features of the signal from massive unlabeled samples. Subsequently, the acquired features are transferred to an improved Siamese network to enhance its robustness and generalization on few-shot datasets. This method not only provides a novel solution for unlabeled signal feature learning but also further promotes the few-shot learning method to become a more robust and practical technique. We verify the proposed method on two fault diagnosis data sets, and the experiments verify that the proposed model achieves excellent performance under extremely limited labeled training samples.