## 6 Discussion and Conclusions
IT and OT are converging in terms of their functionalities,leading to the Industrial IoT through the realization of Cyber-Physical System-of-Systems (CPSoS), where DevOps (Development and Operations) and security-enhanced DevSecOps methodologies enable flexible, reliable, and timely completion of related processes [84]. Finally, this paper contributes to the industrial deployment of the proposed models by promoting a reference framework and a convenient set of tools, reducing the model maintenance efforts that are frequently required from a practical point of view. In the context of Industry 4.0, the proposed monitoring and prediction system can be practically deployed within smart manufacturing environments by leveraging edge computing, industrial IoT platforms, and standardized communication protocols. Sensor data from critical components, such as bearings, pumps, or motors, can be collected using embedded devices or industrial gateways and transmitted in real time through MQTT or OPC-UA protocols. The data pre-processing and predictive modeling stages can be deployed either at the edge for low-latency decisions or in a centralized cloud-based infrastructure for deeper analytics. Integration with existing SCADA or MES platforms enables seamless visualization and operational interfacing. Furthermore, the modular design of the system supports scalable deployment across different machines or plants, and its outputs (fault indicators, remaining-useful-life estimates, etc.) can be fed into reinforcement learning agents or rule-based decision systems to automate prescriptive maintenance actions. This alignment with industry 4.0 principles: interoperability, real-time analytics, and adaptive control positions the system as a practical and forward-compatible solution for predictive and prescriptive maintenance in modern industrial settings. After being positively addressed with the RQs, it is possible to emphasize that ML models are capable of predicting system values quite accurately, which is relevant not just for asset maintenance but other areas as well. However, one must consider that the accuracy of predictions is closely dependent on time, with predictions being much more accurate for values closer in time. As noted above, one of the most sensitive factors of pumping systems is the state of the bearings. From the temperature modeling using the case study data, it would be possible to incorporate alarms indicating when the temperature of bearings exceeds threshold values because the LSTM time series model can predict temperatures far in the future. The data measured in the pumping system case correspond to information on the functioning of the machine in question under operating conditions. However, other types of information can be collected, wherein ML techniques can be helpful for PdM activities. Similarly, the proposed reference framework can become useful for other areas, such as quality monitoring at the asset level, because of the flexibility of the framework and the tools used to allow such expansion. Some researchers have employed data on failures, maintenance needs, or singularities to use classification algorithms that detect and classify a problem. However, this paper does not consider this case because we have not yet collected enough data to express degradation or sudden failures in the asset components, but this topic will be addressed in the future, particularly by using cyclic spectral analysis [85] and with the help of the proposed framework. In conclusion, this work makes a significant contribution to the field by offering a scalable, sustainable, and operationally viable solution for predictive maintenance that ensures the long-term effectiveness of deployed models. By addressing the critical need for automated model updating and continuous performance assessment, this research provides a solid foundation for future advancements in industrial predictive maintenance and supports the broader objective of achieving truly autonomous and intelligent maintenance systems.