A multi-agent cognitive digital twin architecture with neurosymbolic diagnostic integration for railway maintenance

Abstract

Modern railway maintenance increasingly requires diagnostic systems that not only detect anomalies but also align decisions with established engineering standards and remain consistent across inspection cycles; however, many existing solutions rely either on operational alarm thresholds or on purely data-driven detectors, with limited integration between numerical inference and domain semantics. This study proposes a Multi-Agent Cognitive Digital Twin (MA-CDT) architecture designed to distribute cognitive responsibilities across specialised agents linked through shared semantic representations to support traceable information flow between physical inspection processes and digital reasoning components. To provide an initial implementation of the proposed architecture, its diagnostic component was instantiated and empirically evaluated using real operational track-geometry inspection data. The implemented module integrates neural anomaly detection, engineering intervention thresholds and ontology-guided semantic validation within a single decision structure. Comparative experiments against an operational alarm baseline and a neural-only configuration show that the integrated design reduces unsupported alerts while preserving threshold-defined intervention detections under noisy and imbalanced sensing conditions. Ablation analysis isolates the contributions of neural inference, rule-based constraints and semantic validation, demonstrating how their structured interaction stabilises decision behaviour. The diagnostic case study illustrates how one cognitive agent can operate within the proposed semantic framework and exchange information through the architectural knowledge structures. The results provide an initial empirical validation of the diagnostic component as a proof of concept within the proposed MA-CDT architecture and suggest that embedding domain constraints into neurosymbolic diagnostic reasoning could enhance decision stability and traceability in threshold-driven maintenance systems.