## 4 DT-Based Gearbox Monitoring: A Framework for Future Research  
This framework paves the way for innovative research contributions that will further refine and enhance DT-based gearbox monitoring techniques. 1. The development of a DT for monitoring combined faults in gears and bearings is a realistic approach, as these faults often interact in real-world systems. The vibrational or acoustic signatures of these faults can overlap, making their detection and diagnosis more complex. A DT allows for modeling these interactions and a better understanding of the phenomena of fault signature dominance. This paves the way for more robust detection algorithms capable of distinguishing faults even in the presence of noise or parasitic signals. 2. Beyond classical faults (gears and bearings), it is crucial to integrate less-studied but equally critical faults into DTs, such as shaft misalignment or structural nonlinearities. These faults can have a significant impact on the overall system dynamics, causing abnormal vibrations, accelerated wear, or even catastrophic failures. A DT capable of simulating these faults would allow for better anticipation of their effects and optimization of maintenance strategies. 3. Leveraging DTs to optimize sensor placement is a major contribution. By identifying strategic points where fault signatures are most visible, the number of required sensors can be reduced, lowering installation and maintenance costs. Additionally, this limits the volume of data to collect, store, and analyze while improving monitoring efficiency. 4. Integrating complex physical phenomena, such as heat propagation due to excessive friction, enhances the capabilities of DTs. By combining thermal models with data from thermal cameras, it becomes possible to remotely monitor component conditions and detect thermal anomalies before they lead to failures. This enables more precise predictive maintenance and reduces the risk of unexpected breakdowns. 5. The DT approach can be extended to other rotating machines, such as turbines, compressors, or electric motors. Each type of machine has its own dynamic and operational specificities (rotational speed, applied loads, environment, etc. ), requiring model adaptation. For example, turbines may be subject to variable aerodynamic loads, while compressors may experience overpressure phenomena. DTs must incorporate these specificities to be effective. 6. Environmental conditions (wind, temperature, humidity) significantly impact wind turbine dynamics. A DT can simulate these effects to study how they accelerate fault propagation, such as cracks in blades or shafts. Frequent acceleration and deceleration due to wind variations can lead to increased material fatigue. By integrating these factors, DTs enable better failure prediction and optimization of maintenance cycles. * Faults in gears or bearings can lead to energy loss due to reduced transmission efficiency. A DT can estimate this energy loss for each type of fault, even before it becomes critical. This helps define a fault criticality order and prioritize maintenance interventions based on their impact on energy efficiency. * Integrating cost-benefit analysis into DTs allows for evaluating the economic impact of maintenance strategies. This includes modeling maintenance costs, downtime, and component replacement costs. By comparing predictive and corrective maintenance costs, DTs can help identify the most cost-effective strategies and justify investments in advanced monitoring technologies. * Unifying monitoring processes (detection, diagnosis, prognosis) into a single methodology is essential for a coherent approach. DTs can serve as an integrated platform for these three steps, using the same data and models to detect anomalies, diagnose faults, and predict their evolution. This simplifies the monitoring process and improves the accuracy of the prediction. * For high-speed machines, it is crucial to integrate the phenomenon of shaft critical speed into DTs.