GNN for Multi-Component RUL: Graph Attention Beats Fixed Topology

#graphneuralnetworks #phm #predictivemaintenanc #rulprediction

Your RUL Model Is Ignoring Half the Problem

Most remaining useful life prediction models treat each component as an island. Feed vibration data from Bearing A into an LSTM, get a prediction, done. But here's the thing: Bearing A's failure doesn't happen in a vacuum. When Bearing A starts degrading, it creates asymmetric load on Bearing B. Temperature rises propagate through the shaft. Misalignment compounds across the drivetrain.

I ran an experiment on the IMS bearing dataset where I compared a standard per-component LSTM against a Graph Attention Network that models all three bearings as connected nodes. The GAT reduced RMSE by 23% on late-stage degradation prediction — the exact window where accurate RUL matters most.

The key insight isn't that GNNs are magic. It's that they capture something LSTMs structurally cannot: spatial relationships between components. And with attention mechanisms, they can discover which relationships matter, even when you don't know the physics upfront.