## 5 Discussion and Future Research Directions
### Challenges and Industry 5.0 Outlook  
Despite recent advances, AI- and ML-driven predictive maintenance systems continue to face persistent challenges that limit their adoption and scalability. A major barrier is the scarcity of labelled data, as most industrial datasets are highly imbalanced and costly to annotate--particularly for rare or incipient faults. Concept drift further complicates model reliability: operational conditions change, components wear out, and signal characteristics evolve over time, requiring constant model adaptation.  
In addition, the computational demands of deep learning--especially for real-time inference on resource-constrained hardware--pose limitations in edge deployments. The need for explainability is another critical factor, especially in safety-critical or regulated industries, where model transparency underpins compliance and operational trust. Integrating these systems into industrial environments also raises challenges related to IoT heterogeneity, privacy concerns, and large-scale data orchestration. Lastly, scalability and robustness must be ensured to withstand harsh operating conditions and deliver tangible returns on investment (see Figure 13).  
Looking forward, aligning predictive maintenance with the guiding principles of Industry 5.0 presents a unique opportunity to address these challenges while promoting more sustainable and human-centred technologies. In this paradigm, automation and efficiency are complemented by human expertise, collaboration, and responsible innovation. Operators and domain experts are dynamically integrated into the feedback loop via intuitive dashboards and explainable AI overlays, enhancing decision making and interpretability.  
Emerging tools such as digital twins and virtual commissioning enable proactive simulation of failure scenarios, including multi-fault conditions, before they occur in production environments. Furthermore, predictive maintenance can contribute directly to sustainability goals by reducing energy consumption, minimizing unnecessary part replacements, and extending equipment life cycles.  
Figure 14 synthesizes the proposed implementation framework for Industry 5.0. Raw sensor data from physical assets are first processed by an edge/fog AI layer with OT-level control, enabling fast, localized inference and closed-loop actuation. Aggregated insights are then sent to a cloud-based federated learning layer for cross-site model retraining, with updated models and policies returned to the edge.  
Simultaneously, preprocessed data feed a digital thread or Product Lifecycle Management (PLM) backbone, enriching it with context such as maintenance history and design constraints. This persistent digital infrastructure supports traceability and decision making across the asset lifecycle. Both the edge and the digital thread push real-time KPIs and alerts to human-centric UIs, closing the loop through intuitive operator interfaces. This layered flow directly addresses the AI/ML challenges discussed earlier--data scarcity, concept drift, computational overhead, explainability, and integration heterogeneity--by combining edge-cloud intelligence with traceable lifecycle data and human-in-the-loop control.