# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 8 Conclusions  
In conclusion, the Regulated Conformal Prognostic framework presented in this paper provides a comprehensive and adaptable approach to PdM under diverse operating conditions and fault modes. By synthesizing DL architectures, conformal theory, and domain-specific constraints, it ensures that RUL estimates are both statistically well-calibrated and physically coherent. The methodology effectively balances accuracy and interval tightness while maintaining desirable coverage properties, ensuring that maintenance planners can rely on the intervals produced without incurring excessive conservatism. The experimental evidence supports the notion that monotonic regulation appreciably enhances the practical utility of RUL forecasts. Specifically, enforcing strictly non-increasing trajectories aligns with real-world degradation processes, mitigating the detrimental effects of sensor noise and abrupt operational shifts. Moreover, the weighted margin estimator stands out for its ability to adapt to local trends in the residual distribution, thereby delivering a compelling blend of coverage stability and predictive adaptability. Overall, the results attest to the efficacy of combining DL architectures and conformal calibration for high-fidelity prognostics, offering clear pathways for integration into industrial PdM workflows. Future research could extend this framework by exploring additional domain constraints, applying more advanced learning strategies to adapt the models across equipment types, or incorporating data augmentation strategies to handle data-scarce regimes. In doing so, the outlined Conformal Prognostic methodology can become an even more versatile and powerful tool for advancing reliability, cost-effectiveness, and safety in complex engineering systems.  
Conceptualization, A.M., C.C. and R.C.G.; methodology, A.M. and R.C.G.; software, A.M. and R.C.G.; validation, F.M. and C.C.; formal analysis, F.M. and C.C.; investigation, R.C.G. and A.M.; resources, A.M. and R.C.G.; writing--original draft preparation, A.M., R.C.G. and C.C.; writing--review and editing, A.M. and F.M.; visualization, F.M. and R.C.G.; supervision, F.M.; project administration, F.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by PNRR MUR Project PE0000013-FAIR. The FAIR project is committed to promoting an advanced vision of Artificial Intelligence, driving research and development in this crucial field and constantly keeping ethical, legal and sustainability considerations in mind.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6](https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6)]([https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6](https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6)) (accessed on 27 May 2025). The complete results and source code for reproducibility are publicly archived on GitHub at the following link: [[https://github.com/[PERSON]/Conformal-Predictive-Maintenance](https://github.com/[PERSON]/Conformal-Predictive-Maintenance)]([https://github.com/[PERSON]/Conformal-Predictive-Maintenance](https://github.com/[PERSON]/Conformal-Predictive-Maintenance)) (accessed on 27 May 2025)  
**Conflicts of Interest:** The authors declare no conflicts of interest.