In practical applications, it is difficult to acquire sufficient fault samples for training deep learning fault diagnosis model of rolling bearing. Aiming at the few-shot issue and multi-label attributes of single-point faults, a novel fault diagnosis method of rolling bearing based on time–frequency signature matrix (T–FSM) feature and multi-label convolutional neural network with meta-learning (MLCML) is proposed in this paper. At the beginning, the T–FSM features sensitive to few-shot fault diagnosis of measured vibration signal are extracted. Subsequently, a designed multi-label convolutional neural network (MLCNN) with a specific architecture is employed to identify faults. Crucially, the meta-learning strategy of learning initial network parameters susceptive to task changes is incorporated to MLCNN for addressing the few-shot problem. Ultimately, the publicly available rolling bearing dataset is utilized to demonstrate the effectiveness of the proposed method. The experimental results exhibit that the trained MLCML has the capability of learning to learn few-shot fault attributes with outstanding diagnosis accuracy and generalization. More concretely, the model can adapt to new fault categories rapidly owing to that only a few samples and update steps are required to fine-tune the network.

在实际应用中，很难获得足够的故障样本来训练滚动轴承的深度学习故障诊断模型。针对单点故障的少发问题和多标签属性，基于时频特征矩阵（T–FSM）特征和元学习的多标签卷积神经网络的滚动轴承故障诊断新方法（MLCML）在本文中提出。首先，提取对测量的振动信号的几次故障诊断敏感的T–FSM特征。随后，采用具有特定架构的设计的多标签卷积神经网络（MLCNN）来识别故障。至关重要的是，将学习易受任务更改影响的初始网络参数的元学习策略合并到MLCNN中，以解决少数问题。最终，利用公众可获得的滚动轴承数据集来证明所提出方法的有效性。实验结果表明，训练有素的MLCML具有学习少量故障属性的能力，具有出色的诊断准确性和泛化能力。更具体地说，该模型可以快速适应新的故障类别，这是因为仅需几个样本和更新步骤即可对网络进行微调。