FusionXNet edge for predictive maintenance under cross domain deployment shift

Abstract Industrial predictive maintenance (PdM) requires models that remain computationally manageable while handling heterogeneous sensor data and changing operating conditions. This study evaluates FusionXNet-Edge, a compact multi-branch architecture that combines CNN, BiLSTM, GRU, and MLP components, with an auxiliary branch that receives physically motivated engineered features. The methodology assesses the model on the AI4I, MetroPT3, and Engine datasets using leakage-controlled preprocessing, a common single-step input dimensionality, fixed source-domain operating thresholds, and both in-domain and cross-dataset evaluation. The results show competitive in-domain performance while keeping the parameter count at approximately 16 000. Multi-seed ablation experiments show that the auxiliary physics-guided features yield only small, dataset-dependent changes in predictive performance, indicating that most of the model’s capability comes from the compact fusion backbone. Additional component ablations show that simpler variants can remain competitive in selected datasets, particularly under the single-step input configuration. Cross-dataset experiments reveal strongly asymmetric transfer behaviour, and additional schema-alignment diagnostics show that a common dimensional interface does not imply equivalent physical feature semantics across datasets. These findings position FusionXNet-Edge as a compact, deployment-oriented PdM architecture rather than a physics-constrained or universally transferable model. The study also identifies source-target distribution shift, operating-threshold mismatch, and feature-semantic differences as important limitations for cross-dataset deployment.