Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life
  Prediction
Zhixin Huang Yujiang He Bernhard Sick
Remaining useful life prediction plays a crucial role in the health
management of industrial systems. Given the increasing complexity of systems,
data-driven predictive models have attracted significant research interest.
Upon reviewing the existing literature, it appears that many studies either do
not fully integrate both spatial and temporal features or employ only a single
attention mechanism. Furthermore, there seems to be inconsistency in the choice
of data normalization methods, particularly concerning operating conditions,
which might influence predictive performance. To bridge these observations,
this study presents the Spatio-Temporal Attention Graph Neural Network. Our
model combines graph neural networks and temporal convolutional neural networks
for spatial and temporal feature extraction, respectively. The cascade of these
extractors, combined with multi-head attention mechanisms for both
spatio-temporal dimensions, aims to improve predictive precision and refine
model explainability. Comprehensive experiments were conducted on the C-MAPSS
dataset to evaluate the impact of unified versus clustering normalization. The
findings suggest that our model performs state-of-the-art results using only
the unified normalization. Additionally, when dealing with datasets with
multiple operating conditions, cluster normalization enhances the performance
of our proposed model by up to 27%.