  Predicting Remaining Useful Life (RUL) plays a crucial role in the
prognostics and health management of industrial systems that involve a variety
of interrelated sensors. Given a constant stream of time series sensory data
from such systems, deep learning models have risen to prominence at identifying
complex, nonlinear temporal dependencies in these data. In addition to the
temporal dependencies of individual sensors, spatial dependencies emerge as
important correlations among these sensors, which can be naturally modelled by
a temporal graph that describes time-varying spatial relationships. However,
the majority of existing studies have relied on capturing discrete snapshots of
this temporal graph, a coarse-grained approach that leads to loss of temporal
information. Moreover, given the variety of heterogeneous sensors, it becomes
vital that such inherent heterogeneity is leveraged for RUL prediction in
temporal sensor graphs. To capture the nuances of the temporal and spatial
relationships and heterogeneous characteristics in an interconnected graph of
sensors, we introduce a novel model named Temporal and Heterogeneous Graph
Neural Networks (THGNN). Specifically, THGNN aggregates historical data from
neighboring nodes to accurately capture the temporal dynamics and spatial
correlations within the stream of sensor data in a fine-grained manner.
Moreover, the model leverages Feature-wise Linear Modulation (FiLM) to address
the diversity of sensor types, significantly improving the model's capacity to
learn the heterogeneity in the data sources. Finally, we have validated the
effectiveness of our approach through comprehensive experiments. Our empirical
findings demonstrate significant advancements on the N-CMAPSS dataset,
achieving improvements of up to 19.2% and 31.6% in terms of two different
evaluation metrics over state-of-the-art methods.
