Scalable and reliable deep transfer learning for intelligent fault
  detection via multi-scale neural processes embedded with knowledge
Zhongzhi Li Jingqi Tu Jiacheng Zhu Jianliang Ai Yiqun Dong
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.