## 5 Results
However, the accuracy of the model slowly decreases for values greater than 0.5 because the penalty is too large and under-fitting occurs. The optimal value of the model is the one for which both phenomena are minimized. The work performed up to this point provides a good understanding of the models' behavior regarding time horizons as well as hyperparameter settings, enabling data scientists to suggest the appropriate configuration. The findings confirm that even with conventional models such as LSTM and generic RL algorithms, the self-directed framework proposed in this paper can perform with better prediction accuracy and robust prescriptive maintenance plans. They show how the proposed framework can be easily adapted and reveal its cross-domain applicability, which, regardless of any modeling paradigm selected by the practitioners, will assist in facilitating anything they may have decided to implement. As more comprehensive datasets become available and newer sensor technologies provide more robust feature spaces, this system will be able to accommodate more elaborate models, e.g., Transformers, which do not require any structural changes. This quality ensures long-term usability and scalability, further adding to the industrial applicability of the suggested solution. However, this approach is static and limited to the observed dataset, but our goal is to employ them in the future, wherein degradation rates can change and the region of state space where the process is located can evolve. Figure 8: Analysis of the ElasticNet regression model for the pumping system. (**a**) Learning curve. (**b**) Validation curve for \(\alpha\) variation. Figure 7: Performance of the trained LSTM model. (**a**) Residuals from LSTM (time series) and Elasticnet (regressor) models. (**b**) MSE against prediction offset. To address this issue, we propose implementing a cloud-based workflow technology able to manage the continuous measurement of deviations per type of model and, based on these deviations, handle the retraining of the model. The proposed framework pushes toward IT/OT convergence through the capabilities provided by cloud technologies (see Figure 9), and it further complements the proposal found in [75]. Consider continuous data ingestion from a plant or processes concerning cloud-oriented structures. We employed a test configuration using Telegram/Influxdb technology [76], but Kafka [77] or even PI solutions [78], among others, could also be used. NiFi technology, as a workflow management tool, is used to orchestrate all required operations with sufficient flexibility, mainly due to its scalability (see Figure 10) [79]. However, other alternatives, such as airflow(tm) or dagster(tm), among others, can also be used. In this way, not only are remotely driven ingestion procedures allowed, but also others can accept batch processing by uploading the collected data from systems such as IBA to a Minio server [80] and then establishing the appropriate transformations (ETL operations) to feed the data into the time series database. Figure 10: Example of the NiFi workflow defined for DevOps application. Figure 9: Proposed DevOps-compatible framework. From the time-series-oriented database and the model's repository stored in the cloud, time-scheduled predictions for the adopted models (or ensembles) were launched using NiFi workflows, in our case, every five minutes. In fact, we recorded not only individual model predictions but also an aggregated one (using greedy ensemble integration). The prediction for bearing temperature behavior during the next 60 min, made using the ensemble model, could be used for supporting decision-making processes (when the prediction exceeds a threshold, details are sent to different stakeholders, including people responsible for maintenance). The described approach effectively addresses the Ops requirements, but to incorporate the Dev side, additional NiFi workflows were established.