Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs

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Published Oct 26, 2023
Maximilian-Peter Radtke Marco Huber Jürgen Bock

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.

How to Cite

Radtke, M.-P., Huber, M., & Bock, J. (2023). Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3552
Abstract 190 | PDF Downloads 158

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Keywords

Knowledge Graph, Deep Learning, Fault Diagnostics, Transfer Learning, PHM

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Section
Technical Research Papers