伪标签驱动局部子空间对齐的跨域故障诊断方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (20) : 105-113.

论文

张猛1，王波1,2，徐浩1，杨文龙1，汪超1

作者信息 +

A cross-domain fault diagnosis method based on pseudo-label driving local subspace alignment

ZHANG Meng1,WANG Bo1,2,XU Hao1,YANG Wenlong1,WANG Chao1

Author information +

文章历史 +

摘要

由于不同工况下的同类故障特征间存在分布差异，导致了源域中训练的智能诊断模型在应用至目标域时出现性能退化。针对此问题，提出一种伪标签驱动局部子空间对齐，以减少不同域分布差异从而实现跨域故障诊断的方法。首先通过迁移源域中的预训练模型并联合余弦相似度的计算，在模型不同位置为目标域无标签样本计算样本伪标签的概率分布；然后引入局部最大平均差异 (Local maximum mean difference, LMMD)来减少源域和目标域相同子空间的特征分布偏差，实现对齐源域和目标域相关子空间；并且采用宽核卷积神经网络 (Convolutional neural network, CNN) 提取深层次跨域不变特征，最终实现较高故障诊断准确率的跨域智能故障诊断。实验结果表明，在两组验证数据集上所提方法均实现了最高的故障诊断准确率，证明了该方法的优越性，具有较好的实际应用价值。

Abstract

The distribution differences among similar fault features under different operating conditions lead to the performance degradation of the intelligent diagnostic model trained in the source domain when applied to the target domain.. To break the predicament, a pseudo-label driven local subspace alignment method for cross-domain fault diagnosis is proposed. Firstly, by migrating the pre-trained model and combining it with the cosine similarity calculation, the pseudo-label probability distributions of the unlabeled samples in the target domain are calculated at different locations of the model. Then, local maximum mean difference (LMMD) is introduced to reduce the deviation of feature distribution in the same subspace of source and target domains, in order to align the relevant subspaces of source and target domains. Furthermore, the convolutional neural network (CNN) with wide convolution kernels is used to extract deeper cross-domain invariant features, which realizes cross-domain intelligent fault diagnosis with high fault diagnosis accuracy. The proposed method achieves the highest fault diagnosis accuracy on both validation data sets, which entirely proves the superiority of the method and has preferable application value.

导出引用

张猛1，王波1,2，徐浩1，杨文龙1，汪超1. 伪标签驱动局部子空间对齐的跨域故障诊断方法[J]. 振动与冲击, 2023, 42(20): 105-113

ZHANG Meng1,WANG Bo1,2,XU Hao1,YANG Wenlong1,WANG Chao1. A cross-domain fault diagnosis method based on pseudo-label driving local subspace alignment[J]. Journal of Vibration and Shock, 2023, 42(20): 105-113

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