Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs

Abstract

In the realm of PHM, it is common to possess not only process data but also domain knowledge, which, if integrated into data-driven algorithms, can aid in solving specific tasks.This paper explores the integration of knowledge graphs (KGs) into deep learning models to develop a more resilient approach capable of handling domain shifts, such as variations in machine operation conditions.We present and assess a KG-enhanced deep learning approach in a representative PHM use case, demonstrating its effectiveness by incorporating domain-invariant knowledge through the KG.Furthermore, we provide guidance for constructing a comprehensive hierarchical KG representation that preserves semantic information while facilitating numerical representation.The experimental results showcase the improved performance and domain shift robustness of the KG-enhanced approach in fault diagnostics.