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In the Industrial Internet of Things (IIoT), condition monitoring sensorsignals from complex systems often exhibit nonlinear and stochasticspatial-temporal dynamics under varying conditions. These complex dynamics makefault detection particularly challenging. While previous methods effectivelymodel these dynamics, they often neglect the evolution of relationships betweensensor signals. Undetected shifts in these relationships can lead tosignificant system failures. Furthermore, these methods frequently misidentifynovel operating conditions as faults. Addressing these limitations, we proposeDyEdgeGAT (Dynamic Edge via Graph Attention), a novel approach for early-stagefault detection in IIoT systems. DyEdgeGAT's primary innovation lies in a novelgraph inference scheme for multivariate time series that tracks the evolutionof relationships between time series, enabled by dynamic edge construction.Another key innovation of DyEdgeGAT is its ability to incorporate operatingcondition contexts into node dynamics modeling, enhancing its accuracy androbustness. We rigorously evaluated DyEdgeGAT using both a synthetic dataset,simulating varying levels of fault severity, and a real-world industrial-scalemultiphase flow facility benchmark with diverse fault types under varyingoperating conditions and detection complexities. The results show thatDyEdgeGAT significantly outperforms other baseline methods in fault detection,particularly in the early stages with low severity, and exhibits robustperformance under novel operating conditions.

DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems