NEXAR-Maint: a neurosymbolic explainable augmented reality framework for predictive maintenance and troubleshooting in Industry 4.0

Abstract The rapid evolution of Industry 4.0 and the convergence of Information Technology (IT) and Operational Technology (OT) have increased the complexity of industrial maintenance and troubleshooting. Cyber-Physical Production Systems (CPPS) generate large volumes of heterogeneous data, creating opportunities for predictive maintenance but also increasing the need for transparent and context-aware decision support. Existing maintenance solutions remain limited by fragmented workflows, black-box AI models, and Augmented Reality (AR) applications that primarily provide static procedural guidance without explainable reasoning. This paper presents NEXAR-Maint, a proof-of-concept framework that integrates neurosymbolic Artificial Intelligence (AI), Artificial Visual Intelligence (AVI), digital twins, Fault Tree Analysis (FTA), Root Cause Analysis (RCA), and AR to support explainable predictive maintenance and troubleshooting. The framework combines data-driven prediction with symbolic reasoning to provide context-aware diagnostics, while AR serves as the human–machine interface for visualising maintenance guidance and diagnostic pathways within the operator's workspace. To demonstrate the feasibility of the proposed architecture, an exploratory proof-of-concept user study involving three participants was conducted in a controlled maintenance troubleshooting scenario. The evaluation focused on the usability and feasibility of the explainable interaction concepts rather than on establishing statistically generalisable performance improvements. The results demonstrate the technical feasibility of integrating neurosymbolic AI, AVI, and AR into a unified maintenance framework and provide preliminary evidence that explainable AR-based decision support is perceived as useful by participants. These findings serve as a foundation for future large-scale industrial validation involving professional maintenance operators.