  Predicting the Remaining Useful Life (RUL) is essential in Prognostic Health
Management (PHM) for industrial systems. Although deep learning approaches have
achieved considerable success in predicting RUL, challenges such as low
prediction accuracy and interpretability pose significant challenges, hindering
their practical implementation. In this work, we introduce a Spatio-temporal
Attention-based Hidden Physics-informed Neural Network (STA-HPINN) for RUL
prediction, which can utilize the associated physics of the system degradation.
The spatio-temporal attention mechanism can extract important features from the
input data. With the self-attention mechanism on both the sensor dimension and
time step dimension, the proposed model can effectively extract degradation
information. The hidden physics-informed neural network is utilized to capture
the physics mechanisms that govern the evolution of RUL. With the constraint of
physics, the model can achieve higher accuracy and reasonable predictions. The
approach is validated on a benchmark dataset, demonstrating exceptional
performance when compared to cutting-edge methods, especially in the case of
complex conditions.
