# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 1 Introduction
In addressing this issue, [PERSON] et al. (2008) have contributed to the field by introducing the CMAPSS dataset [21] along with simulation paradigms and guidelines. This dataset, developed through damage propagation modeling for aircraft engine run-to-failure simulation, has become a pivotal resource for researchers and practitioners in the field of prognostics and health management, allowing for the realistic simulation and testing of PdM models and providing a vital tool at the disposal of the scientific community. Nonetheless, the field of PdM is still in need of more generalized AI research, as mentioned by [PERSON] et al. (2020), which discusses the potential and challenges of deploying DL within prognostics and health management, emphasizing the need for future research to address the complex patterns in the data and effectively implement data-driven strategies [22]. Additionally, [PERSON] et al. (2015) highlight the slow adoption rate of AI-powered prognostic strategies in decision-making, remarking on the significant gap between their potential benefits and their practical applications, highlighting the need for an intermediate framework that translates data into actionable maintenance decisions, offering more explicit guidance on practical maintenance actions [23]. While these areas have seen significant progress, a comprehensive methodological framework for reliable PdM tasks is still needed. Punctual or multi-horizon forecasting poses stringent limitations on the practical usage of the PdM algorithm in real-world conditions, both when integrated into larger optimization procedures (involving multiple components, plants, and other data sources) and even as an effective DSS serving engineers and domain experts. Moreover, sensor misreading, noise, and missingness can amplify the negative impact of these practices in a real setting, remarking the need for a more reliable solution that extends the algorithm's forecasting power. UQ has recently emerged as an interesting practice in time series analysis and forecasting. It refers to the unpredictability associated with future values of a data sequence due to various factors such as randomness, external influences, and incomplete information. Managing and quantifying uncertainty in time series forecasting is crucial for decision-making processes across diverse domains, including PdM, permitting the construction of a more sophisticated predictive framework that is even more essential when applied to industrial critical components or workflows. In this context, Conformal Prediction is established solidly among the most widely used techniques for estimating confidence intervals due to its practical potential (it does not require explicit assumptions about the predictive algorithm) and flexibility (it works without a predefined data distribution), opening a new potential era for refined DSS supplied by a reliable CI. This work leverages a comprehensive overview that highlights the extensive development of research of IoT systems in PdM to offer a methodological approach to algorithm selection and CI evaluation, as presented in Figure 1, offering a tailored framework for robust IoT-enabled PdM, leading to more efficient and secure maintenance operations. In detail, the study aims to show that when sensor streams exhibit non-stationary behavior and the underlying degradation process is explicitly modeled, embedding a clamp-regulated, one-sided Conformal Prediction layer into the RUL-estimation pipeline produces statistically valid yet markedly tighter lower-bound uncertainty intervals than representative distribution-based benchmarks. Figure 1: Graphical Overview of the robust IoT-enabled PdM framework. This manuscript is articulated in the following sections:  
* Section 1 Introduction--motivation and problem statement
* Section 2 Time series analysis in predictive maintenance--survey of RUL models. * Section 3 CI estimation in predictive maintenance--limits of current UQ methods. * Section 4 CMAPSS dataset--benchmark dataset description.