The consistent probability distribution between training & testing data is one of the prerequisites for valid intelligent diagnosis models. Nevertheless, the ineluctable distribution discrepancies produced by variable working conditions cause intense conflicts with the above. Besides, many existing models fail to explicitly characterize the correlation between data, which makes them difficult to mine discriminative features in limited samples. Fortunately, the proposal of graph neural networks provides a potential solution to overcome the above issues. Inspired by this, this paper creatively proposes a graph architecture search network (GASN) for cross-domain fault diagnosis. Its main framework consists of graph-feature extraction modules, graph-architecture search modules and differentiated classifiers, which can effectively reduce the computational complexity and accelerate the inferential efficiency of GASN, with two distinctive characteristics: (1) Specially designed search module utilizes multi-head probsparse-attention mechanism to search the optimal graph-architecture, thus suppressing the redundancy and exclusivity among candidate graph-data; (2) Triplet loss-driven domain adversarial training strategy is proposed to enhance the domain adaptability of GASN, which can assist itself to achieve fine-grained adaptation of distinguishable architectures in response to unknown domains. Comparative results on four case studies indicate that the GASN can achieve superior performance to existing state-of-art models even under imbalanced data.

训练和测试数据之间一致的概率分布是有效智能诊断模型的先决条件之一。然而，工作条件的变化所产生的不可避免的分配差异与上述情况产生了激烈的冲突。此外，许多现有模型未能明确表征数据之间的相关性，这使得它们难以在有限的样本中挖掘判别特征。幸运的是，图神经网络的提出为克服上述问题提供了一种潜在的解决方案。受此启发，本文创造性地提出了一种用于跨域故障诊断的图架构搜索网络（GASN）。其主要框架由图特征提取模块、图架构搜索模块和差异化分类器组成，probsparse -attention 机制搜索最优图架构，从而抑制候选图数据之间的冗余和排他性；(2) 提出了三元组损失驱动的域对抗训练策略，以增强 GASN 的域适应性，从而帮助自身实现可区分架构对未知域的细粒度适应。四个案例研究的比较结果表明，即使在数据不平衡的情况下，GASN 也可以实现优于现有最先进模型的性能。