  Accurate bearing load monitoring is essential for their Prognostics and
Health Management (PHM), enabling damage assessment, wear prediction, and
proactive maintenance. While bearing sensors are typically placed on the
bearing housing, direct load monitoring requires sensors inside the bearing
itself. Recently introduced sensor rollers enable direct bearing load
monitoring but are constrained by their battery life. Data-driven virtual
sensors can learn from sensor roller data collected during a batterys lifetime
to map operating conditions to bearing loads. Although spatially distributed
bearing sensors offer insights into load distribution (e.g., correlating
temperature with load), traditional machine learning algorithms struggle to
fully exploit these spatial-temporal dependencies. To address this gap, we
introduce a graph-based virtual sensor that leverages Graph Neural Networks
(GNNs) to analyze spatial-temporal dependencies among sensor signals, mapping
existing measurements (temperature, vibration) to bearing loads. Since
temperature and vibration signals exhibit vastly different dynamics, we propose
Heterogeneous Temporal Graph Neural Networks (HTGNN), which explicitly models
these signal types and their interactions for effective load prediction. Our
results demonstrate that HTGNN outperforms Convolutional Neural Networks
(CNNs), which struggle to capture both spatial and heterogeneous signal
characteristics. These findings highlight the importance of capturing the
complex spatial interactions between temperature, vibration, and load.
