Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems
Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-relevant information and reduce model interpretability. This work proposes a semantic feature segmentation framework that decomposes the monitored feature space into a canonical component,expected to retain the dominant predictive information, and a residual component containing structurally peripheral signals. The segmentation is defined through domain informed criteria and sets up monitoring variables into functional groups reflecting operational mechanisms such as throughput,latency,pressure,network activity,and structural state. To evaluate ...
| Authors | Emilio Mastriani, Alessandro Costa, Federico Incardona et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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