Xiao Qiyang, Yang Maolin, Yan Jiayuan, Shi Wentao
School of Artificial Intelligence, Henan University, Zhengzhou, 450046, Henan, China.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, Shanxi, China.
Sci Rep. 2024 Dec 28;14(1):30848. doi: 10.1038/s41598-024-81489-6.
In real engineering scenarios, the complex and variable operating conditions of mechanical equipment lead to distributional differences between the collected fault data and the training data. This distribution difference can lead to the failure of deep learning-based diagnostic models. Extracting generalized diagnostic knowledge from the source domain in scenarios where the target domain is not visible is the key to solving this problem. To this end, in this paper, we propose a domain generalization network for diagnosing bearing faults under unknown operating conditions, i.e., Feature Decoupled Integrated Domain Generalization Network (FDIDG). First, we propose a "feature decoupling" algorithm to uncover generalized representations of fault features from multiple source domains. The algorithm aims to explore the generalized representations of fault features by shrinking the distribution of data from multiple source domains and further generalize the fault features to unknown domains to reduce the coupling between fault features and operating conditions. Second, the diagnostic accuracy of the model under unknown operating conditions is further improved by adopting a multi-expert integration strategy in the decision-making stage and utilizing domain-private features to reduce the negative impact of edge samples on diagnosis. We conducted several sets of cross-domain experiments on both public and private datasets, and the results show that FDIDG has excellent generalization capabilities.
在实际工程场景中，机械设备复杂多变的运行条件导致采集到的故障数据与训练数据之间存在分布差异。这种分布差异会导致基于深度学习的诊断模型失效。在目标域不可见的场景中从源域提取广义诊断知识是解决这一问题的关键。为此，本文提出了一种用于未知运行条件下轴承故障诊断的域泛化网络，即特征解耦集成域泛化网络（FDIDG）。首先，我们提出一种“特征解耦”算法，从多个源域中揭示故障特征的广义表示。该算法旨在通过缩小来自多个源域的数据分布来探索故障特征的广义表示，并进一步将故障特征泛化到未知域，以减少故障特征与运行条件之间的耦合。其次，通过在决策阶段采用多专家集成策略并利用域私有特征来减少边缘样本对诊断的负面影响，进一步提高了模型在未知运行条件下的诊断准确性。我们在公共数据集和私有数据集上进行了多组跨域实验，结果表明FDIDG具有出色的泛化能力。
