极限标签下基于解耦特征伪标签传播的故障诊断

面对实际工程中标签稀少,尤其是单类样本仅 1 个标签的极限标签场景,现有半监督诊断方法的故障识别能力严重不 足。 为此,本文提出一种基于解耦特征伪标签传播算法的半监督故障诊断方法。 首先,引入局部选择的并行集成异常检测方法 分离故障样本;其次,提出基于解耦特征的伪标签传播算法,通过解耦对抗自编码器获得增强的故障特征,进而通过故障特征降 维、特征分布伪质心标定与距离度量实现高效伪标签传播;最后,利用伪标签故障样本训练故障分类器,结合异常检测实现高准 确率故障诊断。 两个旋转部件数据集上的实验结果表明,所提方法在单类故障标签数量为 1 时,同工况和跨工况实验下的平均 诊断准确率分别超过 97% 和 90% ,明显优于对比方法。

Fault diagnosis based on the decoupled feature pseudo-label propagation under extreme labeling scenarios

Faced with the limited labeled sample problem in practical engineering, particularly in extreme labeling scenarios where only one labeled sample is available for each fault type, the existing semi-supervised diagnosis methods suffer from a significant deficiency in the fault identification ability. To address this issue, a novel semi-supervised fault diagnosis method based on the decoupled feature pseudo-label propagation (DFPP) algorithm is proposed. Firstly, the locally selective combination in the parallel outlier ensembles (LSCP) method is introduced to separate fault samples. Subsequently, the DFPP method is proposed. In DFPP, the adversarial decoupled auto-encoder (ADAE) is applied to extract the enhanced fault features, and the incorporation of fault feature dimension reduction, pseudo-centroid calibration of feature distribution, and distance measurement are adopted to efficiently achieve pseudo-label propagation in situations. Finally, a fault classifier is trained by using pseudo-labeled fault samples, and the combination of anomaly detection ensures accurate fault diagnosis with high precision. Experimental results conducted on two datasets of rotating components demonstrate that the proposed method can achieve average diagnostic accuracies exceeding 97% and 90% in the same working condition and cross working condition with extremely limited labeled samples, respectively, which is significantly superior to the comparison methods.