Deep transfer learning (DTL) is a fundamental method in the field of
Intelligent Fault Detection (IFD). It aims to mitigate the degradation of
method performance that arises from the discrepancies in data distribution
between training set (source domain) and testing set (target domain).
Considering the fact that fault data collection is challenging and certain
faults are scarce, DTL-based methods face the limitation of available
observable data, which reduces the detection performance of the methods in the
target domain. Furthermore, DTL-based methods lack comprehensive uncertainty
analysis that is essential for building reliable IFD systems. To address the
aforementioned problems, this paper proposes a novel DTL-based method known as
Neural Processes-based deep transfer learning with graph convolution network
(GTNP). Feature-based transfer strategy of GTNP bridges the data distribution
discrepancies of source domain and target domain in high-dimensional space.
Both the joint modeling based on global and local latent variables and sparse
sampling strategy reduce the demand of observable data in the target domain.
The multi-scale uncertainty analysis is obtained by using the distribution
characteristics of global and local latent variables. Global analysis of
uncertainty enables GTNP to provide quantitative values that reflect the
complexity of methods and the difficulty of tasks. Local analysis of
uncertainty allows GTNP to model uncertainty (confidence of the fault detection
result) at each sample affected by noise and bias. The validation of the
proposed method is conducted across 3 IFD tasks, consistently showing the
superior detection performance of GTNP compared to the other DTL-based methods.

本文提出了一种基于神经过程和图卷积网络的深度转移学习方法（GTNP），通过特征转移策略在高维空间中消除源领域和目标领域之间的数据分布差异，全局和局部潜在变量的联合建模以及稀疏采样策略减少目标领域中观测数据的需求，多尺度不确定性分析通过全局和局部潜在变量的分布特征获得，从而使 GTNP 能够提供反映方法复杂性和任务难度的定量值，同时允许对每个受到噪声和偏差影响的样本进行不确定性建模（故障检测结果的置信度），在 3 个智能故障检测任务中，验证了所提出方法相较于其他基于深度转移学习的方法具有更好的检测性能。