  In the Industrial Internet of Things (IIoT), condition monitoring sensor
signals from complex systems often exhibit nonlinear and stochastic
spatial-temporal dynamics under varying conditions. These complex dynamics make
fault detection particularly challenging. While previous methods effectively
model these dynamics, they often neglect the evolution of relationships between
sensor signals. Undetected shifts in these relationships can lead to
significant system failures. Furthermore, these methods frequently misidentify
novel operating conditions as faults. Addressing these limitations, we propose
DyEdgeGAT (Dynamic Edge via Graph Attention), a novel approach for early-stage
fault detection in IIoT systems. DyEdgeGAT's primary innovation lies in a novel
graph inference scheme for multivariate time series that tracks the evolution
of relationships between time series, enabled by dynamic edge construction.
Another key innovation of DyEdgeGAT is its ability to incorporate operating
condition contexts into node dynamics modeling, enhancing its accuracy and
robustness. We rigorously evaluated DyEdgeGAT using both a synthetic dataset,
simulating varying levels of fault severity, and a real-world industrial-scale
multiphase flow facility benchmark with diverse fault types under varying
operating conditions and detection complexities. The results show that
DyEdgeGAT significantly outperforms other baseline methods in fault detection,
particularly in the early stages with low severity, and exhibits robust
performance under novel operating conditions.
