## 5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thematic** & & **Key Findings (NLP + Expert Review)** & **Relevance and Potential Impact** \\ \hline Digital Twins & \(\bullet\) & Virtual replicas of industrial components (e.g., mill circuits, turbines) enable scenario testing and real-time fault simulation [58, 59, 60, 61]. & Confirms strong linkage between digital twins and reduced operational risk, aligning with calls for advanced Industry 4.0 adoption. & Confirms strong linkage between digital twins and reduced operational risk, aligning with calls for advanced Industry 4.0 adoption. & Confirms that real-time simulation and sensor fusion can boost reliability and asset longevity. \\  & \(\bullet\) & Load-haul cycle optimization in mining [38, 39, 40, 41]. & Underlines that real-time simulation and sensor fusion can boost reliability and asset longevity. \\  & \(\bullet\) & Seamless IoT integration and AI-driven diagnostics reduce downtime and improve resource allocation [30, 48, 68]. & Supports strategic planning in conclusion, advocating for digital twin deployment to unify data, enhance training, and streamline maintenance. \\  & \(\bullet\) & AR-assisted digital twins provide on-site engineers with real-time visual overlays of performance data [42, 43, 44]. & Emphasizes the value of integrated solutions in addressing complexities of harsh industrial settings. \\ \hline Foundational & \(\bullet\) & Deep neural networks, RNN–LSTM, and hybrid AI approaches dominate recent predictive maintenance advances [31, 56, 86]. & Aligns with conclusion emphasizing synergy of machine learning, sensor data, and secure infrastructures. \\  & \(\bullet\) & Fractional-order PID controllers and chaotic system analysis expand predictive accuracy in nonlinear regimes [8, 9, 75]. & Demonstrates potential for scaling advanced fault detection solutions to entire production chains and multi-physics scenarios. \\  & \(\bullet\) & Distributed ledger technologies (DLT) and secure IoT frameworks bolster data integrity in large-scale deployments [32, 33, 41, 83]. & Highlights the challenge of complex model interpretability, reinforcing the need for XAI for broad acceptance and compliance. \\  & \(\bullet\) & Modular, explainable AI (XAI) ensures user trust and interpretable diagnostics in safety-critical environments [32, 67, 87]. & Conributes to future research paths: domain adaptation, real-time digital twinning, and big-data analytics for robust industrial adoption. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\)mains, such as pipelines and hydropower turbines, demonstrating a growing cross-industry impact [55, 58, 60, 68]. Additionally, the integration of advanced signal processing techniques, such as wavelet-based decomposition, with machine learning has significantly improved the reliability of early crack detection in gearboxes and rotating shafts [66, 72, 74]. Studies incorporating IoT systems into this framework [18, 59] suggest that, when combined with real-time data encryption [32, 34, 83], remote monitoring of hazardous or inaccessible sites can become more secure and resilient. Digital Twins and Asset Management: Centers on _Virtual Modeling in Real-Time_, enabling continuous monitoring and _Optimal Planning of Resources_. Digital twins have been employed to evaluate different scenarios and refine long-term maintenance strategies, facilitating a transition from reactive to preventive interventions [64, 80]. These virtual replicas integrate sensor data, domain knowledge, and AI-based anomaly detection to simulate system performance under varying operational conditions [36, 37, 58, 66]. A key area of development is the use of multi-agent digital twins for load-haul fleets, optimizing the interaction between trucks, shovels, and conveyor belts [38, 39, 40, 41]. AR-based (Augmented Reality) interfaces [42, 43, 44] have also enhanced situational awareness by providing real-time overlays of critical parameters (e.g., vibration levels, temperature gradients) onto physical assets in the field.