🤖 AI Summary
This study addresses the challenge in predictive maintenance of complex systems, where heterogeneity and redundancy among monitoring variables often obscure fault signals and undermine model interpretability. The authors propose a semantic feature segmentation framework that leverages domain knowledge to decompose variables into canonical components—carrying essential predictive signals—and residual components containing marginal information. These components are further grouped semantically according to functional mechanisms such as throughput, latency, and pressure. This approach introduces, for the first time, a domain-driven decomposition of the feature space that preserves semantic meaning while emphasizing fault-relevant features. Experimental results demonstrate that canonical components consistently outperform residual ones across diverse temporal configurations, achieving prediction accuracy comparable to models using all features or PCA-based representations, while exhibiting superior structural cohesion and operational interpretability.
📝 Abstract
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 the effectiveness of this decomposition, we adopt a predictive perspective in which expected predictive risk is used as an operational proxy for task-relevant information. Experimental results obtained through time-aware cross-validation show that the canonical space consistently achieves lower predictive risk than the residual space across multiple temporal configurations, indicating that the semantic segmentation concentrates the most relevant information for fault anticipation. In addition, the canonical segments exhibit significantly stronger intra-segment coherence than inter-segment dependence, and this structural organization remains stable after redundancy reduction. When compared with the full feature space and with a Principal Component Analysis (PCA) representation, the canonical space carries out comparable predictive performance and furthermore preserves the semantic meaning of the original variables. These findings suggest that semantic feature segmentation provides an interpretable and information-preserving decomposition of monitoring signals, enabling competitive predictive performance without sacrificing the operational interpretability required in predictive maintenance applications.