## 5 Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dimension** & \multicolumn{2}{c}{**Main Observations**} & \multicolumn{2}{c}{**Key Gaps and Potential Solutions**} \\ \hline Energy Efficiency and Sustainability & \(\bullet\) & Al-driven fault detection reduces energy loss by preventing equipment failures and down-time [8; 51; 53; 54; 63]. & Challenge: High costs for sensor installations and advanced controllers, especially for smaller operators. & Challenge: High costs for sensor installations and advanced controllers, especially for smaller operators. \\ Energy Efficiency and Sustainability & \(\bullet\) & Hybrid prognostics with physics-based energy models optimize operating “sweet spots” [12; 13; 43]. & Solutions: Deploy scalable IoT nodes (_algae computing_ approach), adopt flexible retrofitting strategies to reduce capital burden [13; 43; 44]. & Opportunity: Enhanced resource optimization with advanced control algorithms that dynamically respond to machine load profiles [8; 9; 12]. \\ Sectorial Applications and Critical Failures & \(\bullet\) & Early detection of gear faults, cracked shafts, and bearing wear [52; 55; 56; 58; 60]. & Challenge: Secure real-time data integration in hazardous or remote zones [32; 34; 83]. & Challenge: Secure real-time data integration in hazardous or remote zones [32; 34; 83]. & Challenges: Leverage cryptographic protocols to ensure data integrity; embed ML in microcontrollers for on-site inference [31]. \\  & \(\bullet\) & Synergy of wavelet-based decomposition and AI reduces false alarms for real-time monitoring [18; 59; 66]. & Opportunity: Cross-industry transfer of advanced fault diagnostics from aerospace/power sectors into mining [19; 20; 42; 43]. \\ \hline Digital Twins and Asset Management & \(\bullet\) & Digital twins enable virtual modeling in real-time and predictive resource planning [36; 60; 61; 68]. & Challenge: Inconsistent data quality and validated physical models hamper robust twin deployment [57; 64]. & Challenge: Inconsistent data quality and validated physical models hamper robust twin deployment [57; 64]. & Solutions: Standardized protocols for data acquisition, cloud-based HPC to handle computational overhead [37; 61]. & Solution: Cloud-based HPC to handle computational overhead [37; 61]. & Solution: Cloud-based HPC to handle computational overhead [37; 61]. & Solution: Computation: Expand digital twin scope to entire production chains (e.g., drilling, blasting, processing) for system-level optimization [36; 37; 58; 66]. \\ \hline Fundamental Technologies and Advanced Methods & \(\bullet\) & Deep learning (LSTM-CNN) excels at extracting temporal-frequency features [31; 36; 73]. & Challenge: Black-box nature of deep neural networks complicates on-site interpretability [32; 34]. & Challenge: Black-box nature of deep neural networks complicates on-site interpretability [32; 34]. & Challenges: Deploy eXplainable AI (XAI) approaches for user trust; unity data standards to facilitate large-scale validations [38; 49; 64; 80]. & Solutions: Deploy eXplainable AI (XAI) approaches for user trust; unity data standards to facilitate large-scale validations [38; 49; 64; 80]. & Solutions: Deploy eXplainable AI (XAI) approaches for user trust; unity data standards to facilitate large-scale validations [38; 49; 64]. & for user trust; unity data standards to facilitate large-scale validations [38; 49; 64]. & Objectability: Incorporate a blockchain or DLT solutions to manage multi-party data while fostering advanced predictive ecosystems [43; 44; 89]. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of key discussion points and future directions in predictive maintenance for mining.