# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 1 Introduction
The final phase of CbM comprises effective continuous monitoring with real-time data flow, intending to trigger alerts and maintenance actions when certain thresholds are exceeded [11], representing a comprehensive, data-driven management system. The CbM approach facilitates the construction of a diagnostic framework capable of timely fault detection, possibly minimizing sudden production interruptions, proactively identifying potential issues and root causes, allowing timely maintenance interventions, and enhancing the reliability and efficiency of manufacturing processes. The integration of modern and complex sensor apparatus has significantly improved the effectiveness of CbM techniques, enhancing the understanding of machinery health and contributing to operational efficiency, making these well-oiled strategies even more adaptable in the actual maintenance context; however, uncertainties inherent in analytics due to the more complex technological processes coupled with the time constraints under which decisions must be made pose significant challenges and limitations to its applicability. These limitations have catalyzed the development of PdM and its sophisticated variants, positioning them as innovative drivers for maintenance management systems promising to improve maintenance strategies and decision-making processes further. Technically, the PdM strategies significantly depend on robust and reliable data flow. Through IoT platforms, the analytics can be conducted at the level of individual machines, manufacturing cells, factories, or across entire networks of factories via cloud solutions. However, these data sets are often fragmented across different systems and exist as isolated data islands, or \"silos\", which feature varying semantics and formats, making them hardly interoperable and challenging to amalgamate within a PdM framework [12]. [PERSON] et al. introduce a digital platform designed explicitly for PdM to address these challenges. This platform provides comprehensive support for implementing PdM applications, minimizing the effort required for deployment. The platform achieves this through middleware functions that streamline data collection, facilitate advanced data analytics, and complete the feedback loop by configuring other systems based on assets' predictive insights [12]. Methodologically, the effectiveness of PdM systems relies heavily on the analytical capabilities provided by various data mining and machine learning techniques. These systems analyze datasets to derive predictive insights about the lifetime of assets, focusing on parameters such as RUL and time to end of life (EoL). Studies by [PERSON] et al. (2018) explore the use of diverse machine learning methods in PdM, emphasizing their crucial role in the field [13]. In this context, neural networks have also been identified as particularly effective for these applications. Generally, in the actual PdM research panorama, several algorithms and learning strategies have been utilized to predict the RUL [14], ranging from SL to DL architectures. Among the principal algorithms found in the literature, are statistical models such as Prophet [15], machine learning techniques like Extreme Gradient Boosting (XGBoost) [16], and other decision tree-based variants. Additionally, DL methodologies like Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Neural Basis Expansion Analysis for Time Series (N-BEATS), and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) have been extensively applied, with significant contributions to PdM outcomes [17; 18; 19; 20]. These approaches underscore the importance of selecting the correct algorithm based on the system and dataset's characteristics. This is crucial, as the performance of these algorithms can significantly affect the accuracy of the RUL predictions and the overall efficiency of the maintenance strategy. However, data scarcity in PdM for critical systems such as aircraft engines is still a significant barrier to developing predictive models in real applications.