## 8 Conclusions and Future Research  
This paper demonstrates that by utilizing existing signals and employing neural networks, redundancy can be achieved, enabling the control of equipment in the event of a communication failure. Furthermore, it illustrates how the use of complex neural networks can predict the actual behavior of a hydrogen compressor and advance maintenance management within organizations. This approach fosters proactivity and predictability, avoiding unnecessary shutdowns, over-costs, the over-control of the process, and the unnecessary use of resources.  
Consistent with these findings, this model demonstrates superior generalization capabilities and provides more robust solutions than conventional forecasting systems when faced with pattern shifts in the data series.  
The analysis of the neural network has helped to understand and interpret the differing behavior of the process by identifying similarities and/or differences between the validation and test data series. Thus, before implementing complex maintenance plans aimed at deploying actions and tools to enhance the predictive capability of models through the adoption of cyber-physical systems, IoT, security, data storage, etc., it is essential to understand the causes of the compressor's differing behavior, without overlooking the human factor as a potential source of variability in the process.  
The use of predictive models, no matter how complex, enhances the understanding of the process and helps establish action plans focused on efficiently improving the maintenance function. It also increases the sustainability of organizational activities by avoidingthe waste of resources on unnecessary actions. However, allowing the system to operate autonomously may lead to undesirable outcomes.  
From the study, it can be inferred that the behavior of the original series does not exactly match the predicted series, as the latter exhibits higher variance. Therefore, in the first stage, this information system is intended to be used as an early warning system, since it is likely to produce false positives, not necessarily requiring the controlled shutdown of the equipment.  
In the second stage, to improve the model's predictive capability, adjustments made by control room operators (tampering) on controllable variables will be studied, as these are currently neither analyzed nor recorded, thereby introducing noise into the model.  
Once predictive performance is improved (reducing variability), the final stage would involve training neural network-based models that incorporate new (multivariable) inputs, transitioning to condition-based maintenance. This would include analyzing parts wear, asset utilization patterns, and integrating additional sensors into the system.  
The scalability of the trained model to other equipment is not anticipated due to the unique nature of the training data, although the same type of neural network architecture with identical hyperparameters may be employed for similar assets.  
S.P.-G.: conceptualization, methodology, investigation, writing--original draft preparation, and visualization; C.G.-G.: conceptualization, methodology, investigation, writing--review and supervision; M.A.S.: conceptualization, methodology, investigation, writing--review and supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research received no external funding.  
Not applicable.  
This paper has been produced within the scope of the doctoral activities carried out by the lead author at the International Doctorate School of the Spanish National Distance-Learning University (EIDUNED_Escuela Internacional de Doctorado de la Universidad Nacional de Educacion a Distancia). The authors are grateful for the support provided by this institution.  
The authors declare no conflicts of interest.