## 5 Conclusions  
This paper has explored the development of monitoring strategies for wind turbine gearboxes, culminating in the application of digital twin technology. By combining DT technology with advanced signal processing and artificial intelligence techniques, it is possible to detect and diagnose faults autonomously and in real time. This is critical to ensuring the reliability and availability of wind turbines by enabling proactive maintenance and minimizing unplanned downtime. This paper reviewed existing frameworks, identified challenges such as data scarcity, and proposed solutions using digital twin technology. In addition, it outlined promising paths for future research, including the development of more advanced digital twin models, the integration of advanced artificial intelligence algorithms, and the application of these techniques to other critical wind turbine components. The results of this review-based investigation will further advance strategies for monitoring the condition of wind turbine gearboxes, thereby improving the efficiency and reliability of wind power generation.  
Several promising perspectives emerge for advancing DT technology in condition monitoring. These include modeling combined and less-studied faults, optimizing sensor placement, and integrating complex phenomena such as heat propagation or critical shaft speeds. Extending DTs to diverse machines and environmental conditions enhances their applicability, while unifying detection, diagnosis, and prognosis improves monitoring coherence. Finally, incorporating energy efficiency analysis and cost-benefit evaluation reinforces DTs as strategic tools for predictive and economically optimized maintenance.  
Conceptualization, H.H., Y.A. and M.B., Methodology, H.H., Y.A. and M.B.; Validation, H.H., Y.A. and M.B.; Formal analysis, H.H., Y.A. and M.B.; Writing--original draft, H.H.; Writing--review & editing, H.H., Y.A. and M.B.; Visualization, H.H., Y.A. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Future Direction** & **Industrial Priority** & **Technology Maturity** & **Main Technical Bottleneck** & **Required Resources** \\ \hline
6 & Integration of environmental conditions (wind, humidity, temperature...) & Medium & Medium & Modeling environmental impact on fault propagation & Environmental sensors + long-term operational datasets \\ \hline
7 & Evaluation of energy loss due to mechanical faults & Medium & Low to Medium & Accurate quantification of loss per fault type & Energy models + instrumented test benches \\ \hline
8 & Cost-benefit analysis integration into DT & High & Medium & Reliable multi-criteria economic modeling & Real maintenance cost data + financial simulation tools \\ \hline
9 & Unification of detection, diagnosis, and prognosis processes & High & Medium to High & Coherent integration in a unified platform & Centralized software architecture + integrated AI \\ \hline
10 & Integration of shaft critical speed for high-speed machines & Medium & Low & Accurate dynamic modeling of flexible shafts & FEM modeling + experimental vibration data \\ \hline
11 & Realistic alignment between simulated and real-world signals & Very High & Low & Gap between DT models and actual gearbox behavior & Precise model calibration + deep learning techniques \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.