Encoding Machine Phase Information into Heterogeneous Graphs for Adaptive Fault Diagnosis

Maximilian-Peter Radtke, Marco F. Huber, Jürgen Bock

Abstract: Machinery fault diagnosis is increasingly reliant on data-driven algorithms, yet struggles with adapting to unseen operating conditions. To address this, we propose integrating phase information into heterogeneous graphs for fault diagnostics with Graph Neural Networks (GNNs). Our method involves identifying the distinct phases that a machine undergoes within a cycle and segmenting the signals accordingly. These segmented signals are then represented in a graph of multiple connected sensor networks with diverse node and edge types. Prior to graph classification with a GNN, individual Convolutional Neural Networks (CNNs) preprocess the node attributes to account for their unique characteristics. The evaluation in a domain adaptation setting demonstrates the effectiveness of our approach, offering insights into improving the robustness and domain adaptability of fault diagnosis models.

External IDs:dblp:conf/etfa/RadtkeHB24